From TO/JS - This section sets some flags that can be tweaked to re-run the analysis with different filtering parameters:
RUN_EXAMPLE: allows discarding alleles that have a
frequency below a threshold (defaults to FALSE),
WITHIN_INDIVIDUAL_ALLELE_FREQ_THR: allows discarding
alleles that have a frequency below a threshold (defaults to 0),
BENCHMARK_MARKERS: list of markers to include in the
analysis (defaults to the full list established by Jason, could be
reduced down to 3 for memory optimization),
MAX_MOI_TO_INCLUDE: allows discarding participant data when
they exhibit very complex infection patterns, counted as the total
number of clones across all infection events (defaults to 8),
PRIOR_3RS: a named vector with prior probabilities of
recrudescence (C), relapse (L) or reinfection (I) (defaults to uniform,
i.e. 1/3 each, but tweaked to minimize recrudescence in the present
study).
## RUN_EXAMPLE
## Boolean flag to enable/disable running the example Pv3Rs posterior
## computation on a single episode sampled at random from the dataset.
## DEFAULT: FALSE
RUN_EXAMPLE <- TRUE
## WITHIN_INDIVIDUAL_ALLELE_FREQ_THR
## Minimum threshold above which an allele is preserved in individual
## haplotype data to be preserved when reconstructing infection
## history. Note that this will *not* discard these alleles from the
## population-level allele frequency that is derived from the initial
## visit, but only discard that allele from individual observations
## when it is 'too rare to be exploited'.
## DEFAULT: 0
WITHIN_INDIVIDUAL_ALLELE_FREQ_THR <- 0
## BENCHMARK_MARKERS
## Vector of character string with names matching those from the
## markers of interest for amplicon sequencing. Only the markers
## listed in that vector will be preserved in individual-level data
## and identity of infection will be solely based on the alleles
## observed for these markers.
## The markers are listed by importance as discovered by Jason.
## DEFAULT: all markers (could be suboptimal/too memory-consuming?)
BENCHMARK_MARKERS <- c(
"Chr05",
"Chr07",
"Chr09",
"Chr10",
"Chr08",
"Chr13",
"Chr11",
"Chr03",
"Chr01",
"Chr02",
"Chr14"
)
## MAX_MOI_TO_INCLUDE
## As per Aimee's guidelines:
## We do not recommend running compute_posterior() for data
## whose total genotype count exceeds eight, where the total
## genotype count is the sum of per-episode maximum per-marker
## allele counts.
## The MAX_MOI_TO_INCLUDE expects an integer that will discard all
## individuals having a summed MOI > 8 across all recorded episodes.
## DEFAULT: 8
MAX_MOI_TO_INCLUDE <- 8
## PRIORS_3RS
## A vector of probabilities, summing to 1, corresponding to
## the probability of each stage for the 3Rs for Pv episodes.
## The vector order is re(C)rudescence, re(L)apse, re(I)nfection.
## In this clinical trial, we assume recrudescence is possible so,
## we use the default priors
## DEFAULT: c("C" = 1/3, "L" = 1/3, "I" = 1/3)
# PRIOR_3RS <- c("C" = 1/3, "L" = 1/3, "I" = 1/3)
# Note: the below probabilities were used for SeroTAT study because it does not involve treatment at baseline
# We will use the same for the Peru community survey
PRIOR_3RS <- c("C" = 0.10, "L" = 0.45, "I" = 0.45)
Read in data from Peru:
peru <- read.csv(here("data/final", "merged_PE.csv"))
# head(peru)
# names(peru)
peru %>%
group_by(marker_id) %>%
summarise(unique_haplotypes = n_distinct(haplotype))
## # A tibble: 11 × 2
## marker_id unique_haplotypes
## <chr> <int>
## 1 Chr01 3
## 2 Chr02 4
## 3 Chr03 3
## 4 Chr05 7
## 5 Chr07 6
## 6 Chr08 5
## 7 Chr09 3
## 8 Chr10 4
## 9 Chr11 5
## 10 Chr13 5
## 11 Chr14 4
peru %>% count(marker_id, haplotype)
## marker_id haplotype n
## 1 Chr01 Chr01-1 66
## 2 Chr01 Chr01-2 49
## 3 Chr01 Chr01-3 6
## 4 Chr02 Chr02-1 81
## 5 Chr02 Chr02-2 47
## 6 Chr02 Chr02-3 22
## 7 Chr02 Chr02-4 2
## 8 Chr03 Chr03-1 93
## 9 Chr03 Chr03-2 30
## 10 Chr03 Chr03-3 28
## 11 Chr05 Chr05-1 53
## 12 Chr05 Chr05-2 24
## 13 Chr05 Chr05-3 17
## 14 Chr05 Chr05-4 14
## 15 Chr05 Chr05-5 7
## 16 Chr05 Chr05-6 5
## 17 Chr05 Chr05-8 2
## 18 Chr07 Chr07-1 70
## 19 Chr07 Chr07-2 48
## 20 Chr07 Chr07-3 23
## 21 Chr07 Chr07-4 12
## 22 Chr07 Chr07-5 7
## 23 Chr07 Chr07-6 2
## 24 Chr08 Chr08-1 45
## 25 Chr08 Chr08-2 46
## 26 Chr08 Chr08-3 24
## 27 Chr08 Chr08-4 7
## 28 Chr08 Chr08-6 2
## 29 Chr09 Chr09-1 63
## 30 Chr09 Chr09-2 67
## 31 Chr09 Chr09-3 15
## 32 Chr10 Chr10-1 71
## 33 Chr10 Chr10-2 21
## 34 Chr10 Chr10-3 10
## 35 Chr10 Chr10-4 3
## 36 Chr11 Chr11-1 56
## 37 Chr11 Chr11-2 40
## 38 Chr11 Chr11-3 24
## 39 Chr11 Chr11-4 18
## 40 Chr11 Chr11-5 15
## 41 Chr13 Chr13-1 67
## 42 Chr13 Chr13-2 38
## 43 Chr13 Chr13-3 16
## 44 Chr13 Chr13-4 13
## 45 Chr13 Chr13-5 6
## 46 Chr14 Chr14-1 73
## 47 Chr14 Chr14-2 66
## 48 Chr14 Chr14-3 13
## 49 Chr14 Chr14-4 3
peru %>%
group_by(PatientName, marker_id) %>%
summarise(n_haplotypes = n(), .groups = 'drop') %>%
group_by(PatientName) %>%
summarise(max_moi = max(n_haplotypes))
## # A tibble: 93 × 2
## PatientName max_moi
## <chr> <int>
## 1 M1A007A 2
## 2 M1A007B 3
## 3 M1A007F 2
## 4 M1A008D 3
## 5 M1A020B 3
## 6 M1A020F 2
## 7 M1A033B 2
## 8 M1A037I 2
## 9 M1A040B 2
## 10 M1A043E 4
## # ℹ 83 more rows
Participants with more than one episode for inference (n=40 participants, n=87 Pv isolates):
peru %>%
select(PatientName, episodes) %>%
distinct() %>%
arrange(PatientName) %>%
filter(episodes>1)
## PatientName episodes
## 1 M1A007B 2
## 2 M1A007F 2
## 3 M1A008D 2
## 4 M1A020B 2
## 5 M1A043E 2
## 6 M1A082B 2
## 7 M1A083D 2
## 8 M1A085C 2
## 9 M1A161C 2
## 10 M1B011B 2
## 11 M1B020F 2
## 12 M1B040B 2
## 13 M1C002A 2
## 14 M1C007E 2
## 15 M1C010B 2
## 16 M1C019D 2
## 17 M1C031G 2
## 18 M1C061B 2
## 19 M1C081A 4
## 20 M1C090C 2
## 21 M1D006D 2
## 22 M1D010A 2
## 23 M1D019A 2
## 24 M1D024B 2
## 25 M1D026A 3
## 26 M1D026D 3
## 27 M1D036A 2
## 28 M1D036C 2
## 29 M1D038D 2
## 30 M1D042A 2
## 31 M1D049A 2
## 32 M1D049D 3
## 33 M1E016A 2
## 34 M1E022A 2
## 35 M1E025B 2
## 36 M1E026C 2
## 37 M1E028C 2
## 38 M1E036B 3
## 39 M1E042B 2
## 40 M1E044C 3
ids_recurrent <- peru %>% clean_names() %>% filter(episodes > 1) %>% distinct(patient_name)
peru_recurrent <- peru %>% clean_names() %>% filter(episodes > 1)
# setdiff(ids_recurrent$patient_name, peru_recurrent$patient_name)
# setdiff(peru_recurrent$patient_name, ids_recurrent$patient_name)
# peru_recurrent %>% distinct(sample_id) %>% arrange(sample_id) # 87 isolates
episode_summary <- peru_recurrent %>%
distinct(patient_name, date, sample_id, day) %>%
mutate(date = mdy(date)) %>%
arrange(patient_name, date) %>%
group_by(patient_name) %>%
mutate(
# get the episode number
episode_number = row_number(),
# calculate days since enrolment, just a check with dates
days_since_enrolment = as.integer(date - min(date[day == "Day 0"])),
# calculate days since last episode using lag() and making enrolment episodes 0 days since last
days_since_last_episode = replace_na(as.integer(date - lag(date)), 0)
) %>%
ungroup()
Note that in the Peru study this is not necessarily a person’s “first” episode, rather the first episode in the evaluated study period
episode_summary %>%
distinct(patient_name, sample_id, days_since_enrolment) %>%
ggplot(aes(x = days_since_enrolment, y = reorder(patient_name, days_since_enrolment))) +
geom_line(aes(group = patient_name), color = "darkgrey") +
geom_point() +
labs(x = "Days since first episode during study period",
y = "Participant") +
theme_bw()
We want to remove any haplotypes that appear only once at very low frequencies. Haplotypes should already be filtered to be observed in at least 2 samples and within-host frequency >=1% (in full dataset).
singleton_haps <- peru_recurrent %>%
select(sample_id, marker_id, haplotype, frequency, count) %>%
count(haplotype) %>%
arrange(n) %>%
filter(n==1) %>%
pull(haplotype)
peru_recurrent %>%
filter(haplotype %in% singleton_haps) %>%
ggplot(aes(x = haplotype, y = count)) +
geom_hline(yintercept = 100, linetype = "dashed") +
geom_point(aes(color = frequency, shape = day), size = 3) +
labs(x = "singleton haplotype",
y = "read count",
color = "within-sample frequency",
shape = "timepoint") +
theme_bw() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
peru %>%
filter(haplotype %in% singleton_haps) %>%
ggplot(aes(x = haplotype, y = count)) +
geom_hline(yintercept = 100, linetype = "dashed") +
geom_point(aes(color = frequency, shape = day), size = 3) +
labs(x = "singleton haplotype",
y = "read count",
color = "within-sample frequency",
shape = "timepoint") +
theme_bw() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
## Haplotypes for inference For Peru, most recurrences would be expected
to be either reinfections or relapses, given that this study was an
observational community cohort in two sites in Peru. As per Aimee’s
suggestion, we can estimate population-level allele frequencies based on
all episodes in our dataset (we will use the entire dataset (n=93
participants), not just those the subset that experienced recurrences
(n=40). This should be unbiased as both reinfections and relapses are
draws from the mosquito population (relapses would be time-lagged draws)
in the absence of any systematic within-patient selection of
recrudescent parasites.
# n=1530 haps
all_haps <- peru %>%
pull(haplotype)
peru %>%
select(sample_id, marker_id, haplotype, frequency, count) %>%
arrange(frequency, count)
## sample_id marker_id haplotype frequency count
## 1 AL029017 Chr07 Chr07-1 0.01010 88
## 2 AL025031 Chr11 Chr11-3 0.01015 44
## 3 AC035261 Chr01 Chr01-2 0.01031 22
## 4 AL026178 Chr03 Chr03-1 0.01031 22
## 5 AL029975 Chr02 Chr02-3 0.01031 22
## 6 AC033642 Chr14 Chr14-1 0.01031 44
## 7 AL025336 Chr13 Chr13-2 0.01042 44
## 8 AL033228 Chr03 Chr03-3 0.01045 66
## 9 AC029508 Chr02 Chr02-3 0.01053 44
## 10 AC031719 Chr02 Chr02-1 0.01056 66
## 11 AL032119 Chr03 Chr03-1 0.01064 44
## 12 AL033217 Chr01 Chr01-2 0.01064 44
## 13 AC025519 Chr14 Chr14-1 0.01071 66
## 14 AL033398 Chr08 Chr08-2 0.01075 44
## 15 AC035261 Chr02 Chr02-1 0.01095 66
## 16 AC025519 Chr02 Chr02-1 0.01099 44
## 17 AC028231 Chr07 Chr07-1 0.01102 88
## 18 AL032119 Chr05 Chr05-1 0.01111 44
## 19 AL026659 Chr05 Chr05-5 0.01115 66
## 20 AL030767 Chr03 Chr03-1 0.01119 66
## 21 AC025640 Chr14 Chr14-1 0.01124 44
## 22 AL032765 Chr14 Chr14-3 0.01124 44
## 23 AL030953 Chr01 Chr01-2 0.01136 22
## 24 AL026659 Chr11 Chr11-2 0.01136 44
## 25 AL025230 Chr02 Chr02-2 0.01149 22
## 26 AC032493 Chr07 Chr07-5 0.01149 44
## 27 AL025332 Chr03 Chr03-1 0.01153 88
## 28 AL025332 Chr10 Chr10-3 0.01161 114
## 29 AL026468 Chr08 Chr08-1 0.01170 44
## 30 AC033409 Chr03 Chr03-3 0.01180 88
## 31 AC031997 Chr05 Chr05-4 0.01190 66
## 32 AL030581 Chr03 Chr03-2 0.01195 66
## 33 AC032820 Chr07 Chr07-1 0.01205 66
## 34 AL026649 Chr14 Chr14-1 0.01208 88
## 35 AL024635 Chr03 Chr03-3 0.01217 110
## 36 AC034050 Chr11 Chr11-1 0.01223 88
## 37 AC032947 Chr09 Chr09-1 0.01235 22
## 38 AL031919 Chr09 Chr09-1 0.01242 88
## 39 AC028730 Chr09 Chr09-1 0.01249 33
## 40 AC034050 Chr14 Chr14-2 0.01255 66
## 41 AC035178 Chr10 Chr10-1 0.01261 66
## 42 AL029071 Chr08 Chr08-1 0.01266 22
## 43 AL031919 Chr10 Chr10-1 0.01266 44
## 44 AL032744 Chr14 Chr14-4 0.01266 44
## 45 AC033642 Chr07 Chr07-1 0.01274 88
## 46 AC034279 Chr07 Chr07-2 0.01294 88
## 47 AL025336 Chr01 Chr01-2 0.01316 22
## 48 AL027673 Chr14 Chr14-1 0.01316 66
## 49 AC029508 Chr14 Chr14-2 0.01329 132
## 50 AC025519 Chr13 Chr13-1 0.01339 66
## 51 AL025114 Chr10 Chr10-3 0.01351 88
## 52 AL026649 Chr07 Chr07-5 0.01355 110
## 53 AL029026 Chr10 Chr10-2 0.01383 138
## 54 AL025728 Chr14 Chr14-1 0.01418 44
## 55 AC034470 Chr11 Chr11-5 0.01418 88
## 56 AL028427 Chr11 Chr11-1 0.01449 66
## 57 AL024087 Chr01 Chr01-1 0.01500 66
## 58 AL033098 Chr08 Chr08-1 0.01515 22
## 59 AL033098 Chr08 Chr08-4 0.01515 22
## 60 AC030871 Chr14 Chr14-1 0.01556 88
## 61 AL026738 Chr01 Chr01-2 0.01562 22
## 62 AL029026 Chr14 Chr14-1 0.01565 155
## 63 AL032236 Chr08 Chr08-3 0.01587 22
## 64 AL026659 Chr07 Chr07-2 0.01609 132
## 65 AL031382 Chr09 Chr09-1 0.01636 101
## 66 AC034279 Chr09 Chr09-2 0.01653 44
## 67 AL025075 Chr09 Chr09-3 0.01653 44
## 68 AL033217 Chr11 Chr11-2 0.01660 88
## 69 AC035959 Chr11 Chr11-2 0.01662 132
## 70 AC034529 Chr07 Chr07-4 0.01667 110
## 71 AL030528 Chr02 Chr02-1 0.01676 66
## 72 AL025728 Chr02 Chr02-1 0.01724 132
## 73 AC035261 Chr11 Chr11-2 0.01748 110
## 74 AL025336 Chr03 Chr03-1 0.01780 176
## 75 AL029026 Chr03 Chr03-2 0.01814 176
## 76 AC032492 Chr14 Chr14-3 0.01863 66
## 77 AL029071 Chr11 Chr11-3 0.01899 66
## 78 AL026609 Chr02 Chr02-1 0.01923 22
## 79 AL030528 Chr05 Chr05-2 0.01923 22
## 80 AL031382 Chr01 Chr01-3 0.01923 22
## 81 AL031062 Chr02 Chr02-2 0.01932 88
## 82 AL025765 Chr07 Chr07-5 0.02000 132
## 83 AC035959 Chr01 Chr01-2 0.02143 66
## 84 AL025728 Chr05 Chr05-4 0.02183 110
## 85 AL031366 Chr14 Chr14-2 0.02500 132
## 86 AL031062 Chr05 Chr05-5 0.02647 198
## 87 AL023661 Chr08 Chr08-2 0.02662 154
## 88 AL029026 Chr03 Chr03-1 0.02721 264
## 89 AC025795 Chr02 Chr02-1 0.02797 88
## 90 AC025519 Chr05 Chr05-1 0.02817 44
## 91 AC034050 Chr07 Chr07-1 0.02885 132
## 92 AL026178 Chr07 Chr07-4 0.02907 110
## 93 AL029026 Chr07 Chr07-2 0.02923 287
## 94 AL029026 Chr07 Chr07-1 0.02933 288
## 95 AL032236 Chr01 Chr01-3 0.03057 154
## 96 AC032820 Chr02 Chr02-3 0.03185 220
## 97 AL025336 Chr09 Chr09-2 0.03212 81
## 98 AL032744 Chr02 Chr02-2 0.03265 176
## 99 AL029026 Chr02 Chr02-1 0.03268 110
## 100 AL030634 Chr01 Chr01-2 0.03279 44
## 101 AL031062 Chr11 Chr11-4 0.03358 198
## 102 AL025765 Chr03 Chr03-3 0.03371 336
## 103 AL032236 Chr14 Chr14-2 0.03448 44
## 104 AL026178 Chr11 Chr11-1 0.03529 66
## 105 AC032947 Chr08 Chr08-2 0.03727 132
## 106 AL023661 Chr09 Chr09-1 0.03836 330
## 107 AC032502 Chr11 Chr11-5 0.04167 66
## 108 AL033070 Chr09 Chr09-1 0.04245 198
## 109 AL032437 Chr11 Chr11-1 0.04274 110
## 110 AL023661 Chr14 Chr14-2 0.04453 242
## 111 AL033070 Chr14 Chr14-2 0.04624 176
## 112 AC034050 Chr13 Chr13-2 0.04702 330
## 113 AC031312 Chr09 Chr09-2 0.04930 154
## 114 AL032500 Chr11 Chr11-1 0.05303 154
## 115 AL030953 Chr13 Chr13-4 0.05556 132
## 116 AL032912 Chr01 Chr01-3 0.05618 110
## 117 AL031919 Chr14 Chr14-3 0.05882 330
## 118 AL023661 Chr11 Chr11-5 0.06061 264
## 119 AC034470 Chr10 Chr10-3 0.06122 330
## 120 AL031919 Chr11 Chr11-1 0.06247 603
## 121 AL025031 Chr14 Chr14-4 0.06299 623
## 122 AL031919 Chr03 Chr03-1 0.06312 623
## 123 AL026178 Chr07 Chr07-2 0.06395 242
## 124 AL030953 Chr03 Chr03-1 0.06498 396
## 125 AL029026 Chr09 Chr09-2 0.06533 91
## 126 AC033409 Chr13 Chr13-5 0.06667 22
## 127 AL031448 Chr05 Chr05-1 0.06785 506
## 128 AC030871 Chr02 Chr02-2 0.07322 695
## 129 AL029071 Chr11 Chr11-2 0.07595 264
## 130 AC034470 Chr05 Chr05-1 0.07670 572
## 131 AL032912 Chr01 Chr01-2 0.07865 154
## 132 AL030953 Chr14 Chr14-1 0.08235 308
## 133 AC030871 Chr08 Chr08-6 0.08312 704
## 134 AL023661 Chr02 Chr02-2 0.08322 816
## 135 AL023661 Chr13 Chr13-4 0.08383 308
## 136 AL033070 Chr07 Chr07-1 0.08488 834
## 137 AL031919 Chr13 Chr13-4 0.08696 44
## 138 AL033070 Chr13 Chr13-2 0.09184 198
## 139 AL030953 Chr11 Chr11-2 0.09884 374
## 140 AL023661 Chr07 Chr07-3 0.10122 989
## 141 AL031862 Chr11 Chr11-2 0.10160 418
## 142 AL031919 Chr07 Chr07-2 0.10238 1011
## 143 AL023661 Chr03 Chr03-3 0.11326 902
## 144 AL032437 Chr08 Chr08-2 0.12409 374
## 145 AL032119 Chr14 Chr14-4 0.12676 396
## 146 AL030116 Chr03 Chr03-3 0.12727 924
## 147 AC032820 Chr09 Chr09-1 0.12805 462
## 148 AL031062 Chr03 Chr03-3 0.13220 858
## 149 AL030931 Chr02 Chr02-2 0.13309 814
## 150 AL023661 Chr07 Chr07-2 0.13387 1308
## 151 AL031062 Chr03 Chr03-2 0.13559 880
## 152 AL029071 Chr10 Chr10-1 0.15099 1464
## 153 AC029508 Chr05 Chr05-2 0.15550 1276
## 154 AL030116 Chr07 Chr07-4 0.15672 1386
## 155 AC030871 Chr11 Chr11-1 0.16000 352
## 156 AL030114 Chr05 Chr05-1 0.16107 528
## 157 AC034279 Chr13 Chr13-1 0.16190 374
## 158 AL023661 Chr01 Chr01-1 0.16310 1342
## 159 AL032437 Chr02 Chr02-1 0.16312 1012
## 160 AL028375 Chr13 Chr13-2 0.16583 726
## 161 AL031919 Chr08 Chr08-1 0.16722 1100
## 162 AL031919 Chr02 Chr02-1 0.17042 1166
## 163 AC030871 Chr13 Chr13-2 0.17203 1593
## 164 AL031919 Chr13 Chr13-5 0.17391 88
## 165 AL023523 Chr03 Chr03-2 0.17469 1702
## 166 AL031062 Chr07 Chr07-1 0.18147 1780
## 167 AL030931 Chr07 Chr07-3 0.18279 1795
## 168 AL030116 Chr08 Chr08-3 0.18667 308
## 169 AC029508 Chr10 Chr10-2 0.18919 1232
## 170 AL030581 Chr07 Chr07-1 0.18997 1879
## 171 AL026486 Chr14 Chr14-2 0.19417 1826
## 172 AL025332 Chr14 Chr14-1 0.19551 122
## 173 AL029958 Chr11 Chr11-5 0.20635 572
## 174 AL028375 Chr02 Chr02-4 0.20930 396
## 175 AL031919 Chr07 Chr07-3 0.21408 2114
## 176 AL030116 Chr11 Chr11-4 0.22353 418
## 177 AL029958 Chr01 Chr01-1 0.23622 660
## 178 AL032765 Chr08 Chr08-2 0.24405 902
## 179 AL033070 Chr08 Chr08-1 0.24444 968
## 180 AL030116 Chr09 Chr09-3 0.26486 1078
## 181 AL033098 Chr01 Chr01-1 0.27049 726
## 182 AL032765 Chr01 Chr01-1 0.27778 440
## 183 AL032765 Chr14 Chr14-2 0.29213 1144
## 184 AL029958 Chr14 Chr14-2 0.29831 1936
## 185 AC035959 Chr13 Chr13-3 0.30137 1452
## 186 AL029958 Chr07 Chr07-1 0.30215 2887
## 187 AL033098 Chr09 Chr09-1 0.30736 1562
## 188 AL023661 Chr13 Chr13-2 0.31737 1166
## 189 AL028928 Chr02 Chr02-2 0.31955 1870
## 190 AL029958 Chr08 Chr08-4 0.33108 1078
## 191 AL023661 Chr05 Chr05-4 0.34169 2398
## 192 AC033409 Chr08 Chr08-2 0.35926 2134
## 193 AL030931 Chr03 Chr03-3 0.36220 3455
## 194 AL032437 Chr13 Chr13-4 0.36667 726
## 195 AC031719 Chr09 Chr09-1 0.37143 1430
## 196 AL030116 Chr10 Chr10-2 0.37367 3588
## 197 AL031862 Chr14 Chr14-2 0.39837 1078
## 198 AL032437 Chr03 Chr03-3 0.42735 2200
## 199 AC030871 Chr10 Chr10-2 0.46269 2728
## 200 AL032765 Chr07 Chr07-2 0.46397 4533
## 201 AL026486 Chr13 Chr13-2 0.47929 1782
## 202 AC034279 Chr08 Chr08-1 0.50000 4026
## 203 AC034279 Chr08 Chr08-2 0.50000 4026
## 204 AL026486 Chr13 Chr13-1 0.52071 1936
## 205 AL032765 Chr07 Chr07-1 0.53603 5237
## 206 AC030871 Chr10 Chr10-1 0.53731 3168
## 207 AL032437 Chr03 Chr03-1 0.57265 2948
## 208 AL026178 Chr13 Chr13-3 0.58447 2816
## 209 AL031862 Chr14 Chr14-1 0.58537 1584
## 210 AL023661 Chr13 Chr13-1 0.59880 2200
## 211 AL030116 Chr10 Chr10-1 0.62633 6014
## 212 AL032437 Chr13 Chr13-1 0.63333 1254
## 213 AL030931 Chr03 Chr03-2 0.63780 6084
## 214 AC033409 Chr08 Chr08-1 0.64074 3806
## 215 AL023661 Chr05 Chr05-1 0.65831 4620
## 216 AL029958 Chr08 Chr08-2 0.66892 2178
## 217 AL028928 Chr02 Chr02-1 0.68045 3982
## 218 AL031919 Chr07 Chr07-1 0.68354 6750
## 219 AL033098 Chr09 Chr09-2 0.69264 3520
## 220 AL032765 Chr14 Chr14-1 0.69663 2728
## 221 AL029958 Chr07 Chr07-2 0.69785 6668
## 222 AC035959 Chr13 Chr13-1 0.69863 3366
## 223 AL029958 Chr14 Chr14-1 0.70169 4554
## 224 AL032765 Chr01 Chr01-2 0.72222 1144
## 225 AL033098 Chr01 Chr01-2 0.72951 1958
## 226 AL030116 Chr09 Chr09-2 0.72973 2970
## 227 AL031062 Chr03 Chr03-1 0.73220 4752
## 228 AC033409 Chr13 Chr13-1 0.73333 242
## 229 AL031919 Chr13 Chr13-3 0.73913 374
## 230 AL032437 Chr08 Chr08-1 0.74453 2244
## 231 AL033070 Chr08 Chr08-2 0.75556 2992
## 232 AL032765 Chr08 Chr08-4 0.75595 2794
## 233 AL029958 Chr01 Chr01-2 0.76378 2134
## 234 AL023661 Chr07 Chr07-1 0.76492 7474
## 235 AL030116 Chr11 Chr11-5 0.77647 1452
## 236 AL028375 Chr02 Chr02-3 0.77907 1474
## 237 AL033029 Chr13 Chr13-1 0.79032 2156
## 238 AL029958 Chr11 Chr11-2 0.79365 2200
## 239 AL025332 Chr14 Chr14-2 0.80449 502
## 240 AL026486 Chr14 Chr14-1 0.80583 7578
## 241 AL030581 Chr07 Chr07-2 0.81003 8012
## 242 AC029508 Chr10 Chr10-1 0.81081 5280
## 243 AL031062 Chr07 Chr07-2 0.81323 7977
## 244 AL030116 Chr08 Chr08-4 0.81333 1342
## 245 AL030931 Chr07 Chr07-1 0.81721 8025
## 246 AL023523 Chr03 Chr03-3 0.82531 8041
## 247 AL031919 Chr08 Chr08-6 0.82609 5434
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## 1125 AL029958 Chr10 Chr10-1 1.00000 6512
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## 1128 AC034050 Chr08 Chr08-2 1.00000 6578
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## 1130 AL030912 Chr01 Chr01-1 1.00000 6600
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## 1150 AC035333 Chr14 Chr14-2 1.00000 6820
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## 1160 AC031997 Chr08 Chr08-2 1.00000 6930
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## 1168 AC032502 Chr05 Chr05-1 1.00000 7018
## 1169 AC032502 Chr13 Chr13-1 1.00000 7018
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## 1171 AL030912 Chr09 Chr09-3 1.00000 7055
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## 1174 AL033217 Chr10 Chr10-1 1.00000 7062
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## 1177 AL032340 Chr02 Chr02-1 1.00000 7084
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## 1180 AL030114 Chr10 Chr10-1 1.00000 7150
## 1181 AL032066 Chr09 Chr09-2 1.00000 7172
## 1182 AL028427 Chr09 Chr09-1 1.00000 7175
## 1183 AC028730 Chr13 Chr13-1 1.00000 7194
## 1184 AL028928 Chr13 Chr13-3 1.00000 7194
## 1185 AC034279 Chr05 Chr05-1 1.00000 7216
## 1186 AL030328 Chr09 Chr09-1 1.00000 7230
## 1187 AC031997 Chr13 Chr13-1 1.00000 7238
## 1188 AL027673 Chr10 Chr10-1 1.00000 7238
## 1189 AL025743 Chr07 Chr07-1 1.00000 7260
## 1190 AL025736 Chr09 Chr09-1 1.00000 7271
## 1191 AL025336 Chr02 Chr02-1 1.00000 7282
## 1192 AL025114 Chr03 Chr03-3 1.00000 7304
## 1193 AL030671 Chr03 Chr03-2 1.00000 7304
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## 1195 AL026647 Chr07 Chr07-1 1.00000 7326
## 1196 AC027188 Chr03 Chr03-1 1.00000 7348
## 1197 AL033228 Chr10 Chr10-1 1.00000 7370
## 1198 AC025540 Chr03 Chr03-1 1.00000 7392
## 1199 AL031366 Chr02 Chr02-1 1.00000 7392
## 1200 AL027682 Chr02 Chr02-2 1.00000 7414
## 1201 AL030541 Chr03 Chr03-1 1.00000 7414
## 1202 AC035159 Chr13 Chr13-1 1.00000 7436
## 1203 AL025075 Chr07 Chr07-2 1.00000 7436
## 1204 AL032715 Chr05 Chr05-6 1.00000 7436
## 1205 AC025640 Chr10 Chr10-2 1.00000 7458
## 1206 AC032820 Chr03 Chr03-1 1.00000 7458
## 1207 AC032820 Chr05 Chr05-1 1.00000 7458
## 1208 AC034470 Chr07 Chr07-1 1.00000 7458
## 1209 AL030500 Chr03 Chr03-2 1.00000 7458
## 1210 AL026178 Chr09 Chr09-1 1.00000 7476
## 1211 AC025540 Chr01 Chr01-1 1.00000 7480
## 1212 AC030405 Chr05 Chr05-1 1.00000 7480
## 1213 AL030237 Chr13 Chr13-2 1.00000 7480
## 1214 AL033396 Chr01 Chr01-2 1.00000 7480
## 1215 AL032765 Chr02 Chr02-1 1.00000 7502
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## 1217 AL031382 Chr14 Chr14-1 1.00000 7524
## 1218 AL030552 Chr03 Chr03-1 1.00000 7568
## 1219 AL030581 Chr05 Chr05-1 1.00000 7568
## 1220 AC031312 Chr10 Chr10-2 1.00000 7590
## 1221 AL028294 Chr14 Chr14-1 1.00000 7590
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## 1223 AL025743 Chr14 Chr14-1 1.00000 7634
## 1224 AL030237 Chr03 Chr03-1 1.00000 7634
## 1225 AL024180 Chr08 Chr08-1 1.00000 7656
## 1226 AL032715 Chr03 Chr03-3 1.00000 7656
## 1227 AL025515 Chr13 Chr13-2 1.00000 7661
## 1228 AC031719 Chr07 Chr07-3 1.00000 7678
## 1229 AL026659 Chr08 Chr08-1 1.00000 7678
## 1230 AL030073 Chr10 Chr10-3 1.00000 7678
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## 1232 AL026609 Chr07 Chr07-1 1.00000 7700
## 1233 AC034279 Chr03 Chr03-1 1.00000 7722
## 1234 AL024087 Chr13 Chr13-1 1.00000 7737
## 1235 AL025728 Chr10 Chr10-1 1.00000 7766
## 1236 AC033016 Chr05 Chr05-1 1.00000 7788
## 1237 AC033642 Chr03 Chr03-1 1.00000 7788
## 1238 AL023523 Chr13 Chr13-3 1.00000 7789
## 1239 AC035159 Chr01 Chr01-2 1.00000 7810
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## 1241 AL026486 Chr02 Chr02-1 1.00000 7832
## 1242 AL031382 Chr02 Chr02-1 1.00000 7854
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## 1245 AL025114 Chr14 Chr14-2 1.00000 7898
## 1246 AC035333 Chr03 Chr03-2 1.00000 7920
## 1247 AL025743 Chr09 Chr09-2 1.00000 7920
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## 1250 AL026884 Chr03 Chr03-2 1.00000 7942
## 1251 AL029071 Chr14 Chr14-2 1.00000 7942
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## 1253 AL024563 Chr02 Chr02-2 1.00000 7964
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## 1255 AL028361 Chr13 Chr13-1 1.00000 7986
## 1256 AL029975 Chr03 Chr03-2 1.00000 7986
## 1257 AL033396 Chr05 Chr05-5 1.00000 7986
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## 1266 AL027357 Chr11 Chr11-1 1.00000 8162
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## 1429 AL030116 Chr14 Chr14-2 1.00000 9870
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## 1449 AL027757 Chr14 Chr14-1 1.00000 9890
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## 1470 AL028964 Chr14 Chr14-1 1.00000 9905
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## 1482 AL033029 Chr07 Chr07-2 1.00000 9923
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## 1485 AL032744 Chr07 Chr07-4 1.00000 9925
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## 1487 AL026486 Chr03 Chr03-2 1.00000 9929
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## 1489 AL031908 Chr07 Chr07-1 1.00000 9929
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## 1491 AC027188 Chr02 Chr02-1 1.00000 9933
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## 1493 AL028971 Chr14 Chr14-3 1.00000 9934
## 1494 AL030291 Chr03 Chr03-1 1.00000 9934
## 1495 AL031862 Chr07 Chr07-2 1.00000 9935
## 1496 AL027757 Chr03 Chr03-2 1.00000 9937
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## 1504 AL028971 Chr03 Chr03-2 1.00000 9943
## 1505 AL030114 Chr03 Chr03-1 1.00000 9943
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## 1508 AL025728 Chr07 Chr07-1 1.00000 9954
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## 1512 AL028928 Chr14 Chr14-1 1.00000 9958
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## 1514 AL028361 Chr07 Chr07-1 1.00000 9963
## 1515 AL033396 Chr10 Chr10-1 1.00000 9963
## 1516 AC028730 Chr03 Chr03-1 1.00000 9966
## 1517 AL028375 Chr03 Chr03-3 1.00000 9967
## 1518 AL033029 Chr02 Chr02-1 1.00000 9970
## 1519 AL033182 Chr07 Chr07-2 1.00000 9974
## 1520 AL028361 Chr02 Chr02-1 1.00000 9976
## 1521 AL030912 Chr07 Chr07-2 1.00000 9976
## 1522 AC035959 Chr03 Chr03-1 1.00000 9978
## 1523 AL026884 Chr07 Chr07-1 1.00000 9978
## 1524 AL023630 Chr02 Chr02-1 1.00000 9982
## 1525 AL031382 Chr10 Chr10-1 1.00000 9983
## 1526 AL030065 Chr03 Chr03-1 1.00000 9985
## 1527 AL026178 Chr02 Chr02-3 1.00000 9989
## 1528 AL023630 Chr07 Chr07-1 1.00000 10000
## 1529 AL025765 Chr02 Chr02-3 1.00000 10000
## 1530 AL027877 Chr14 Chr14-1 1.00000 10000
analysis_data <- peru_recurrent %>%
select(subject_id = patient_name,
sample_id,
community = com_y,
timepoint = day,
visit_date = date,
age_years = age,
sex,
episodes,
marker_id,
haplotype,
frequency,
mean_moi,
max_moi) %>%
# add extra epi info on episode number and time since last episode
left_join(episode_summary %>% select(subject_id = patient_name,
sample_id,
episode_number,
days_since_enrolment,
days_since_last_episode),
by = c("subject_id", "sample_id")) %>%
# ensure dates are date class
mutate(visit_date = mdy(visit_date))
test_df <- peru %>%
# filter data frame by marker
filter(marker_id == "Chr05")
test_df %>%
mutate(haplotype = factor(haplotype)) %>%
select(sample_id, haplotype, frequency) %>%
pivot_wider(names_from = haplotype,
values_from = frequency,
values_fill = 0) %>%
pivot_longer(cols = -sample_id,
names_to = "haplotype",
values_to = "frequency") %>%
# Get population-level haplotype frequency,
# correcting for when within-individual sum is not equal
# to 1, as can happen when a minority clone is <2%
group_by(sample_id) %>%
mutate(frequency = frequency / sum(frequency, na.rm = TRUE)) %>%
# get population-level mean freq
group_by(haplotype) %>%
summarise(poplevel_mean_freq = mean(frequency, na.rm = TRUE)) %>%
adorn_totals("row")
## Derive pop-level allele frequency - This is modified from Thomas/Jason script for our data
# Use data from the entire dataset
fs <- peru %>%
# split data frame by marker
group_by(marker_id) %>%
group_split() %>%
# Derive a within-marker list of frequencies, by individual
lapply(function(x) {
x <- x %>%
# Build a within-individual frequency table that
# always includes every haplotype (even the ones absent)
mutate(haplotype = factor(haplotype)) %>%
select(sample_id, haplotype, frequency) %>%
pivot_wider(names_from = haplotype,
values_from = frequency,
values_fill = 0) %>%
pivot_longer(cols = -sample_id,
names_to = "haplotype",
values_to = "frequency") %>%
# Get population-level haplotype frequency,
# correcting for when within-individual sum is not equal
# to 1, as can happen when a minority clone is <2%
group_by(sample_id) %>%
mutate(frequency = frequency / sum(frequency, na.rm = TRUE)) %>%
group_by(haplotype) %>%
summarise(frequency_pop_mean = mean(frequency, na.rm = TRUE))
return(deframe(x))
}) %>%
setNames(nm = peru %>% group_by(marker_id) %>% group_keys() %>% pull(marker_id))
# Here we can also save as dataframe for easier printing and table-ready for paper
fs_df <- peru %>%
# Group by marker_id and sample_id for further calculations
group_by(marker_id, sample_id) %>%
# Build a within-individual frequency table that always includes every haplotype (even the ones absent)
mutate(haplotype = factor(haplotype)) %>%
select(marker_id, sample_id, haplotype, frequency) %>%
pivot_wider(names_from = haplotype, values_from = frequency, values_fill = list(frequency = 0)) %>%
pivot_longer(cols = -c(marker_id, sample_id), names_to = "haplotype", values_to = "frequency") %>%
# Get population-level haplotype frequency, correcting for when within-individual sum is not equal to 1
group_by(marker_id, sample_id) %>%
mutate(frequency = frequency / sum(frequency, na.rm = TRUE)) %>%
group_by(marker_id, haplotype) %>%
summarise(frequency_pop_mean = mean(frequency, na.rm = TRUE), .groups = 'drop') %>%
# Ensure haplotypes are correctly associated with their marker_id - note that this works for us because , in future would have to make this flexible to allow for haplotype names that are not reliant on having marker_id
filter(str_detect(haplotype, marker_id))
fs_df
## # A tibble: 49 × 3
## marker_id haplotype frequency_pop_mean
## <chr> <chr> <dbl>
## 1 Chr01 Chr01-1 0.588
## 2 Chr01 Chr01-2 0.383
## 3 Chr01 Chr01-3 0.0296
## 4 Chr02 Chr02-1 0.541
## 5 Chr02 Chr02-2 0.307
## 6 Chr02 Chr02-3 0.143
## 7 Chr02 Chr02-4 0.00925
## 8 Chr03 Chr03-1 0.648
## 9 Chr03 Chr03-2 0.199
## 10 Chr03 Chr03-3 0.153
## # ℹ 39 more rows
# This has been modified from Thomas/Jason script
## Pick an individual at random to run Pv3Rs
# indiv_name <- sample(unique(analysis_data$subject_id), size = 1)
indiv_name <- "M1A020B"
## Prepare the data
# 1- Subset haplotype data to specific individual and apply filters
indiv_haplotype_data <- analysis_data %>%
# Restrict to a single patient
filter(subject_id == indiv_name) %>%
# Restrict to a subset of markers, if needed
filter(marker_id %in% BENCHMARK_MARKERS) %>%
# Restrict to summed MOI below threshold
group_by(subject_id, episode_number, marker_id) %>%
mutate(MOI_per_marker = sum(n())) %>%
group_by(subject_id, episode_number) %>%
mutate(MOI_per_episode = max(MOI_per_marker, na.rm = TRUE)) %>%
ungroup() %>%
mutate(marker_id = factor(marker_id, levels = BENCHMARK_MARKERS))
# 2- Calculate per-episode and per-participant MOI for PvR3S eligibility
indiv_MOI <- indiv_haplotype_data %>%
select(subject_id, episode_number,
marker_id, starts_with("MOI_")) %>%
distinct() %>%
group_by(subject_id, episode_number) %>%
# Get highest per-marker MOI only for each episode
# (drop marker_id in case of ties with highest per-marker MOI)
select(-marker_id) %>%
distinct() %>%
filter(MOI_per_marker == max(MOI_per_marker, na.rm = TRUE)) %>%
ungroup() %>%
mutate(MOI_summed = sum(MOI_per_episode, na.rm = TRUE))
Prepare test data
# Finish data preparation
indiv_haplotype_data <- indiv_haplotype_data %>%
group_by(episode_number) %>%
group_split() %>%
lapply(function(x) {
res <- x %>%
select(sample_id, episode_number,
marker_id, haplotype, frequency) %>%
# For sensitivity analysis, allow to include/drop allele
# based on their within-individual frequency
filter(frequency >= WITHIN_INDIVIDUAL_ALLELE_FREQ_THR) %>%
select(-sample_id, -episode_number, -frequency) %>%
distinct() %>%
# Prevent dropping of markers that are not characterised
# by setting .drop to FALSE
group_by(marker_id, .drop = FALSE) %>%
group_split() %>%
lapply(function(y) {
unique(y$haplotype)
})
# Returned a list named with each episode,
# setting marker allele to NA in case none are observed
return(lapply(setNames(res, BENCHMARK_MARKERS),
function(y) {
if (length(y) == 0) return(NA) else return(y)
}))
})
# Run Aimee's posterior estimation
indiv_posterior <- Pv3Rs::compute_posterior(y = indiv_haplotype_data,
fs = fs[BENCHMARK_MARKERS])
## Number of valid relationship graphs (RGs) is 39
## =============================================================================|
## Computing log p(Y|RG) for 39 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
indiv_posterior
## $marg
## C L I
## [1,] 0 0.9983208 0.00167925
##
## $joint
## C L I
## 0.00000000 0.99832075 0.00167925
The joint posterior probability of reinfection is 99.8%, when we look at the genetic data, this is in line with the inferred probability of relapse.
peru_recurrent %>%
filter(patient_name == "M1A020B") %>%
ggplot(aes(x = haplotype, y = factor(date), fill = factor(haplotype))) +
geom_tile() +
facet_grid(~marker_id, scales = "free_x") +
theme_bw() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
# This has been modified from Thomas/Jason script
## Pick an individual at random to run Pv3Rs
indiv_name <- sample(unique(analysis_data$subject_id), size = 1)
## Prepare the data
# 1- Subset haplotype data to specific individual and apply filters
indiv_haplotype_data <- analysis_data %>%
# Restrict to a single patient
filter(subject_id == indiv_name) %>%
# Restrict to a subset of markers, if needed
filter(marker_id %in% BENCHMARK_MARKERS) %>%
# Restrict to summed MOI below threshold
group_by(subject_id, episode_number, marker_id) %>%
mutate(MOI_per_marker = sum(n())) %>%
group_by(subject_id, episode_number) %>%
mutate(MOI_per_episode = max(MOI_per_marker, na.rm = TRUE)) %>%
ungroup() %>%
mutate(marker_id = factor(marker_id, levels = BENCHMARK_MARKERS))
# 2- Calculate per-episode and per-participant MOI for PvR3S eligibility
indiv_MOI <- indiv_haplotype_data %>%
select(subject_id, episode_number,
marker_id, starts_with("MOI_")) %>%
distinct() %>%
group_by(subject_id, episode_number) %>%
# Get highest per-marker MOI only for each episode
# (drop marker_id in case of ties with highest per-marker MOI)
select(-marker_id) %>%
distinct() %>%
filter(MOI_per_marker == max(MOI_per_marker, na.rm = TRUE)) %>%
ungroup() %>%
mutate(MOI_summed = sum(MOI_per_episode, na.rm = TRUE))
Now that the data has been subset, Pv3Rs::compute_posterior() will be called (only if the participant meets eligibility criteria as defined by the global variables set earlier in this report).
if (RUN_EXAMPLE & unique(indiv_MOI$MOI_summed) <= MAX_MOI_TO_INCLUDE) {
# Verbose
cat("Running Pv3Rs for: ",
indiv_name,
" (summed MOI = ",
unique(indiv_MOI$MOI_summed),
").\n",
sep = "")
# Finish data preparation
indiv_haplotype_data <- indiv_haplotype_data %>%
group_by(episode_number) %>%
group_split() %>%
lapply(function(x) {
res <- x %>%
select(sample_id, episode_number,
marker_id, haplotype, frequency) %>%
# For sensitivity analysis, allow to include/drop allele
# based on their within-individual frequency
filter(frequency >= WITHIN_INDIVIDUAL_ALLELE_FREQ_THR) %>%
select(-sample_id, -episode_number, -frequency) %>%
distinct() %>%
# Prevent dropping of markers that are not characterised
# by setting .drop to FALSE
group_by(marker_id, .drop = FALSE) %>%
group_split() %>%
lapply(function(y) {
unique(y$haplotype)
})
# Returned a list named with each episode,
# setting marker allele to NA in case none are observed
return(lapply(setNames(res, BENCHMARK_MARKERS),
function(y) {
if (length(y) == 0) return(NA) else return(y)
}))
})
# Run Aimee's posterior estimation
indiv_posterior <- Pv3Rs::compute_posterior(y = indiv_haplotype_data,
fs = fs[BENCHMARK_MARKERS])
} else {
# Verbose
cat("NOT Running Pv3Rs for: ",
indiv_name,
" because either summed MOI exceeds threshold (observed = ",
unique(indiv_MOI$MOI_summed),
", MAX_MOI_TO_INCLUDE = ",
MAX_MOI_TO_INCLUDE,
"), or RUN_EXAMPLE was set to FALSE.\n",
sep = "")
# Return NULL
indiv_posterior <- NULL
}
indiv_posterior
peru_recurrent %>%
filter(patient_name == indiv_name) %>%
ggplot(aes(x = haplotype, y = factor(date), fill = factor(haplotype))) +
geom_tile() +
facet_grid(~marker_id, scales = "free_x") +
theme_bw() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
# Start timer
t_start <- Sys.time()
# Initialize an empty list to store the results
indiv_posteriors <- list()
# Loop through each unique individual name
for (indiv_name in unique(analysis_data$subject_id)) {
# Verbose
cat("ID : ", indiv_name, "...\n", sep = "")
## Prepare the data
# 1- Subset haplotype data to specific individual and apply filters
indiv_haplotype_data <- analysis_data %>%
# Restrict to a single patient
filter(subject_id == indiv_name) %>%
# Restrict to a subset of markers
filter(marker_id %in% BENCHMARK_MARKERS) %>%
# Restrict to summed MOI below threshold
group_by(subject_id, episode_number, marker_id) %>%
mutate(MOI_per_marker = sum(n())) %>%
group_by(subject_id, episode_number) %>%
mutate(MOI_per_episode = max(MOI_per_marker, na.rm = TRUE)) %>%
ungroup() %>%
mutate(marker_id = factor(marker_id, levels = BENCHMARK_MARKERS))
# 2- Calculate per-episode and per-participant MOI for PvR3S eligibility
indiv_MOI <- indiv_haplotype_data %>%
select(subject_id, episode_number,
marker_id, starts_with("MOI_")) %>%
distinct() %>%
group_by(subject_id, episode_number) %>%
# Get highest per-marker MOI only for each episode
# (drop marker_id in case of ties with highest per-marker MOI)
select(-marker_id) %>%
distinct() %>%
filter(MOI_per_marker == max(MOI_per_marker, na.rm = TRUE)) %>%
ungroup() %>%
mutate(MOI_summed = sum(MOI_per_episode, na.rm = TRUE))
## Run Pv3Rs only if MOI below threshold
if (unique(indiv_MOI$MOI_summed) <= MAX_MOI_TO_INCLUDE) {
# Verbose
cat("Running Pv3Rs for: ",
indiv_name,
" (summed MOI = ",
unique(indiv_MOI$MOI_summed),
").\n",
sep = "")
# Preserve rounds for naming the output list
indiv_episode <- unique(indiv_haplotype_data$episode_number)
# Finish data preparation
indiv_haplotype_data <- indiv_haplotype_data %>%
group_by(episode_number) %>%
group_split() %>%
lapply(function(x) {
res <- x %>%
select(sample_id, episode_number,
marker_id, haplotype, frequency) %>%
# For sensitivity analysis, allow to include/drop allele
# based on their within-individual frequency
filter(frequency >= WITHIN_INDIVIDUAL_ALLELE_FREQ_THR) %>%
select(-sample_id, -episode_number, -frequency) %>%
distinct() %>%
# Prevent dropping of markers that are not characterized
# by setting .drop to FALSE
group_by(marker_id, .drop = FALSE) %>%
group_split() %>%
lapply(function(y) {
unique(y$haplotype)
})
# Returned a list named with each episode,
# setting marker allele to NA in case none are observed
return(lapply(setNames(res, BENCHMARK_MARKERS),
function(y) {
if (length(y) == 0) return(NA) else return(y)
}))
})
# Run Aimee's posterior estimation
indiv_posterior <- compute_posterior(y = indiv_haplotype_data,
fs = fs[BENCHMARK_MARKERS],
prior = matrix(PRIOR_3RS,
nrow = length(indiv_haplotype_data),
ncol = length(PRIOR_3RS),
byrow = TRUE,
dimnames = list(c(1:length(indiv_haplotype_data)),
names(PRIOR_3RS))))
} else {
# Verbose
cat("NOT Running Pv3Rs for: ",
indiv_name,
" because summed MOI exceeds threshold (observed = ",
unique(indiv_MOI$MOI_summed),
", MAX_MOI_TO_INCLUDE = ",
MAX_MOI_TO_INCLUDE,
").\n",
sep = "")
# Return NULL
indiv_episode <- unique(indiv_haplotype_data$episode_number)
indiv_posterior <- NULL
}
# Append the results to the list
indiv_posteriors[[indiv_name]] <- list("subject_id" = indiv_name,
"episode_number" = indiv_episode,
"Pv3Rs" = indiv_posterior)
}
## ID : M1A008D...
## Running Pv3Rs for: M1A008D (summed MOI = 4).
## Number of valid relationship graphs (RGs) is 39
## =============================================================================|
## Computing log p(Y|RG) for 39 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : M1A083D...
## Running Pv3Rs for: M1A083D (summed MOI = 3).
## Number of valid relationship graphs (RGs) is 9
## =============================================================================|
## Computing log p(Y|RG) for 9 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : M1A007B...
## Running Pv3Rs for: M1A007B (summed MOI = 4).
## Number of valid relationship graphs (RGs) is 39
## =============================================================================|
## Computing log p(Y|RG) for 39 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : M1A007F...
## Running Pv3Rs for: M1A007F (summed MOI = 2).
## Number of valid relationship graphs (RGs) is 3
## =============================================================================|
## Computing log p(Y|RG) for 3 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : M1B020F...
## Running Pv3Rs for: M1B020F (summed MOI = 3).
## Number of valid relationship graphs (RGs) is 9
## =============================================================================|
## Computing log p(Y|RG) for 9 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : M1B040B...
## Running Pv3Rs for: M1B040B (summed MOI = 4).
## Number of valid relationship graphs (RGs) is 39
## =============================================================================|
## Computing log p(Y|RG) for 39 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : M1A085C...
## Running Pv3Rs for: M1A085C (summed MOI = 2).
## Number of valid relationship graphs (RGs) is 3
## =============================================================================|
## Computing log p(Y|RG) for 3 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : M1A161C...
## Running Pv3Rs for: M1A161C (summed MOI = 3).
## Number of valid relationship graphs (RGs) is 9
## =============================================================================|
## Computing log p(Y|RG) for 9 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : M1A082B...
## Running Pv3Rs for: M1A082B (summed MOI = 3).
## Number of valid relationship graphs (RGs) is 9
## =============================================================================|
## Computing log p(Y|RG) for 9 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : M1A020B...
## Running Pv3Rs for: M1A020B (summed MOI = 4).
## Number of valid relationship graphs (RGs) is 39
## =============================================================================|
## Computing log p(Y|RG) for 39 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : M1B011B...
## Running Pv3Rs for: M1B011B (summed MOI = 3).
## Number of valid relationship graphs (RGs) is 9
## =============================================================================|
## Computing log p(Y|RG) for 9 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : M1A043E...
## Running Pv3Rs for: M1A043E (summed MOI = 4).
## Number of valid relationship graphs (RGs) is 39
## =============================================================================|
## Computing log p(Y|RG) for 39 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : M1D049D...
## Running Pv3Rs for: M1D049D (summed MOI = 6).
## Number of valid relationship graphs (RGs) is 1315
## =============================================================================|
## Computing log p(Y|RG) for 1315 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : M1C019D...
## Running Pv3Rs for: M1C019D (summed MOI = 3).
## Number of valid relationship graphs (RGs) is 9
## =============================================================================|
## Computing log p(Y|RG) for 9 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : M1D042A...
## Running Pv3Rs for: M1D042A (summed MOI = 2).
## Number of valid relationship graphs (RGs) is 3
## =============================================================================|
## Computing log p(Y|RG) for 3 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : M1C081A...
## Running Pv3Rs for: M1C081A (summed MOI = 7).
## Number of valid relationship graphs (RGs) is 11088
## =============================================================================|
## Computing log p(Y|RG) for 11088 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : M1C002A...
## Running Pv3Rs for: M1C002A (summed MOI = 2).
## Number of valid relationship graphs (RGs) is 3
## =============================================================================|
## Computing log p(Y|RG) for 3 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : M1C061B...
## Running Pv3Rs for: M1C061B (summed MOI = 3).
## Number of valid relationship graphs (RGs) is 9
## =============================================================================|
## Computing log p(Y|RG) for 9 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : M1D010A...
## Running Pv3Rs for: M1D010A (summed MOI = 2).
## Number of valid relationship graphs (RGs) is 3
## =============================================================================|
## Computing log p(Y|RG) for 3 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : M1D024B...
## Running Pv3Rs for: M1D024B (summed MOI = 4).
## Number of valid relationship graphs (RGs) is 30
## =============================================================================|
## Computing log p(Y|RG) for 30 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : M1D026A...
## Running Pv3Rs for: M1D026A (summed MOI = 5).
## Number of valid relationship graphs (RGs) is 250
## =============================================================================|
## Computing log p(Y|RG) for 250 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : M1E044C...
## Running Pv3Rs for: M1E044C (summed MOI = 6).
## Number of valid relationship graphs (RGs) is 1565
## =============================================================================|
## Computing log p(Y|RG) for 1565 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : M1D019A...
## Running Pv3Rs for: M1D019A (summed MOI = 4).
## Number of valid relationship graphs (RGs) is 39
## =============================================================================|
## Computing log p(Y|RG) for 39 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : M1C007E...
## Running Pv3Rs for: M1C007E (summed MOI = 2).
## Number of valid relationship graphs (RGs) is 3
## =============================================================================|
## Computing log p(Y|RG) for 3 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : M1D026D...
## Running Pv3Rs for: M1D026D (summed MOI = 6).
## Number of valid relationship graphs (RGs) is 1315
## =============================================================================|
## Computing log p(Y|RG) for 1315 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : M1E016A...
## Running Pv3Rs for: M1E016A (summed MOI = 3).
## Number of valid relationship graphs (RGs) is 9
## =============================================================================|
## Computing log p(Y|RG) for 9 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : M1E025B...
## Running Pv3Rs for: M1E025B (summed MOI = 4).
## Number of valid relationship graphs (RGs) is 39
## =============================================================================|
## Computing log p(Y|RG) for 39 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : M1D036C...
## Running Pv3Rs for: M1D036C (summed MOI = 4).
## Number of valid relationship graphs (RGs) is 39
## =============================================================================|
## Computing log p(Y|RG) for 39 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : M1D049A...
## Running Pv3Rs for: M1D049A (summed MOI = 3).
## Number of valid relationship graphs (RGs) is 9
## =============================================================================|
## Computing log p(Y|RG) for 9 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : M1E036B...
## Running Pv3Rs for: M1E036B (summed MOI = 5).
## Number of valid relationship graphs (RGs) is 250
## =============================================================================|
## Computing log p(Y|RG) for 250 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : M1E026C...
## Running Pv3Rs for: M1E026C (summed MOI = 2).
## Number of valid relationship graphs (RGs) is 3
## =============================================================================|
## Computing log p(Y|RG) for 3 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : M1E028C...
## Running Pv3Rs for: M1E028C (summed MOI = 2).
## Number of valid relationship graphs (RGs) is 3
## =============================================================================|
## Computing log p(Y|RG) for 3 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : M1C031G...
## Running Pv3Rs for: M1C031G (summed MOI = 4).
## Number of valid relationship graphs (RGs) is 39
## =============================================================================|
## Computing log p(Y|RG) for 39 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : M1E042B...
## Running Pv3Rs for: M1E042B (summed MOI = 2).
## Number of valid relationship graphs (RGs) is 3
## =============================================================================|
## Computing log p(Y|RG) for 3 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : M1E022A...
## Running Pv3Rs for: M1E022A (summed MOI = 4).
## Number of valid relationship graphs (RGs) is 39
## =============================================================================|
## Computing log p(Y|RG) for 39 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : M1D038D...
## Running Pv3Rs for: M1D038D (summed MOI = 4).
## Number of valid relationship graphs (RGs) is 30
## =============================================================================|
## Computing log p(Y|RG) for 30 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : M1C010B...
## Running Pv3Rs for: M1C010B (summed MOI = 4).
## Number of valid relationship graphs (RGs) is 30
## =============================================================================|
## Computing log p(Y|RG) for 30 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : M1D036A...
## Running Pv3Rs for: M1D036A (summed MOI = 2).
## Number of valid relationship graphs (RGs) is 3
## =============================================================================|
## Computing log p(Y|RG) for 3 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : M1C090C...
## Running Pv3Rs for: M1C090C (summed MOI = 5).
## Number of valid relationship graphs (RGs) is 172
## =============================================================================|
## Computing log p(Y|RG) for 172 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : M1D006D...
## Running Pv3Rs for: M1D006D (summed MOI = 2).
## Number of valid relationship graphs (RGs) is 3
## =============================================================================|
## Computing log p(Y|RG) for 3 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
# End timer
t_end <- Sys.time()
cat("Pv3Rs for the whole dataset took : ", as.numeric(difftime(time1 = t_end,
time2 = t_start,
units = "secs"))/60, " mins", "\n", sep = "")
## Pv3Rs for the whole dataset took : 0.2346241 mins
# Present the marginal data in a clearer format
indiv_posteriors_marginal <- do.call(rbind,
lapply(indiv_posteriors, function(x) {
if (!is.null(x[["Pv3Rs"]])) {
return(data.frame("subject_id" = x[["subject_id"]],
"episode_number" = x[["episode_number"]][-1],
"Posterior_marginal_prob_C" = x[["Pv3Rs"]]$marg[, "C"],
"Posterior_marginal_prob_L" = x[["Pv3Rs"]]$marg[, "L"],
"Posterior_marginal_prob_I" = x[["Pv3Rs"]]$marg[, "I"]))
} else {
return(NULL)
}
}))
row.names(indiv_posteriors_marginal) <- 1:nrow(indiv_posteriors_marginal)
# Present the joint posterior estimates in a clearer format
indiv_posteriors_joint <- do.call(rbind,
lapply(indiv_posteriors, function(x) {
if (!is.null(x[["Pv3Rs"]])) {
joint_probs <- x[["Pv3Rs"]]$joint
# Extract the state pairs and probabilities
state_pairs <- names(joint_probs)
prob_values <- as.numeric(joint_probs)
# Create a data frame with subject_id, episode_number, and joint probabilities
return(data.frame("subject_id" = rep(x[["subject_id"]], length(state_pairs)),
"episode_number" = rep(x[["episode_number"]][-1], each = length(state_pairs)),
"state_pair" = state_pairs,
"joint_probability" = prob_values))
} else {
return(NULL)
}
}))
row.names(indiv_posteriors_joint) <- 1:nrow(indiv_posteriors_joint)
# Save Pv3Rs output because it's time consuming and
# we don't want to re-run it every time.
save(list = c("RUN_EXAMPLE", "WITHIN_INDIVIDUAL_ALLELE_FREQ_THR", "BENCHMARK_MARKERS", "MAX_MOI_TO_INCLUDE", "PRIOR_3RS",
"analysis_data", "fs",
"indiv_posteriors", "indiv_posteriors_marginal", "indiv_posteriors_joint"),
file = paste0("./outputs/Pv3Rs_peru_posteriors_",
strftime(Sys.time(), format = "%Y%m%d_%H%M%S"),
".RData"))
The marginal probabilities give us the probability of the three states for each recurrent episode. However, this does not consider the joint probability of different states when a person experienced more than one recurrent episode.
indiv_posteriors_marginal
## subject_id episode_number Posterior_marginal_prob_C
## 1 M1A008D 2 0.0000000
## 2 M1A083D 2 0.0000000
## 3 M1A007B 2 0.0000000
## 4 M1A007F 2 0.3925671
## 5 M1B020F 2 0.0000000
## 6 M1B040B 2 0.0000000
## 7 M1A085C 2 0.0000000
## 8 M1A161C 2 0.0000000
## 9 M1A082B 2 0.0000000
## 10 M1A020B 2 0.0000000
## 11 M1B011B 2 0.0000000
## 12 M1A043E 2 0.0000000
## 13 M1D049D 2 0.0000000
## 14 M1D049D 3 0.0000000
## 15 M1C019D 2 0.0000000
## 16 M1D042A 2 0.0000000
## 17 M1C081A 2 0.0000000
## 18 M1C081A 3 0.0000000
## 19 M1C081A 4 0.0000000
## 20 M1C002A 2 0.0000000
## 21 M1C061B 2 0.0000000
## 22 M1D010A 2 0.0000000
## 23 M1D024B 2 0.0000000
## 24 M1D026A 2 0.0000000
## 25 M1D026A 3 0.0000000
## 26 M1E044C 2 0.0000000
## 27 M1E044C 3 0.0000000
## 28 M1D019A 2 0.0000000
## 29 M1C007E 2 0.0000000
## 30 M1D026D 2 0.0000000
## 31 M1D026D 3 0.0000000
## 32 M1E016A 2 0.0000000
## 33 M1E025B 2 0.0000000
## 34 M1D036C 2 0.0000000
## 35 M1D049A 2 0.3050571
## 36 M1E036B 2 0.3208016
## 37 M1E036B 3 0.0000000
## 38 M1E026C 2 0.3776514
## 39 M1E028C 2 0.0000000
## 40 M1C031G 2 0.0000000
## 41 M1E042B 2 0.0000000
## 42 M1E022A 2 0.0000000
## 43 M1D038D 2 0.0000000
## 44 M1C010B 2 0.0000000
## 45 M1D036A 2 0.3846383
## 46 M1C090C 2 0.0000000
## 47 M1D006D 2 0.0000000
## Posterior_marginal_prob_L Posterior_marginal_prob_I
## 1 0.09349443 0.9065055681
## 2 0.19037917 0.8096208306
## 3 0.09303113 0.9069688689
## 4 0.60719228 0.0002405764
## 5 0.21863282 0.7813671776
## 6 0.09304682 0.9069531828
## 7 0.25947486 0.7405251391
## 8 0.18211923 0.8178807686
## 9 0.22280060 0.7771994031
## 10 0.99832075 0.0016792496
## 11 0.99848175 0.0015182461
## 12 0.99689703 0.0031029707
## 13 0.06188471 0.9381152932
## 14 0.55668424 0.4433157594
## 15 0.19383355 0.8061664541
## 16 0.25135792 0.7486420841
## 17 0.34132835 0.6586716540
## 18 0.34705495 0.6529450466
## 19 0.14937003 0.8506299712
## 20 0.68892955 0.3110704468
## 21 0.19107623 0.8089237659
## 22 0.28365892 0.7163410818
## 23 0.15492601 0.8450739850
## 24 0.18504804 0.8149519614
## 25 0.05134035 0.9486596501
## 26 0.09454487 0.9054551295
## 27 0.03554051 0.9644594850
## 28 0.16635935 0.8336406488
## 29 0.33769327 0.6623067329
## 30 0.27937051 0.7206294866
## 31 0.03841057 0.9615894348
## 32 0.20790984 0.7920901638
## 33 0.09322659 0.9067734120
## 34 0.09302329 0.9069767081
## 35 0.69142943 0.0035134297
## 36 0.67770238 0.0014959905
## 37 0.07543176 0.9245682361
## 38 0.61812147 0.0042271206
## 39 0.55039121 0.4496087949
## 40 0.09302326 0.9069767394
## 41 0.28093657 0.7190634262
## 42 0.09302326 0.9069767442
## 43 0.14477166 0.8552283441
## 44 0.14798465 0.8520153488
## 45 0.61520379 0.0001579491
## 46 0.36994567 0.6300543307
## 47 0.54277193 0.4572280717
The joint probabilites give us values for each possible combination of states, depending on the number of recurrent episodes experienced by the participant.
joint_summary <- indiv_posteriors_joint %>%
group_by(subject_id, state_pair, joint_probability) %>%
filter(episode_number == max(episode_number)) %>%
mutate(percentage = round(joint_probability*100, 3),
total_recurrences = episode_number-1) %>%
select(-episode_number) %>%
arrange(subject_id, total_recurrences, percentage) %>%
relocate(total_recurrences, .before = state_pair)
joint_summary
## # A tibble: 174 × 5
## # Groups: subject_id, state_pair, joint_probability [174]
## subject_id total_recurrences state_pair joint_probability percentage
## <chr> <dbl> <chr> <dbl> <dbl>
## 1 M1A007B 1 C 0 0
## 2 M1A007B 1 L 0.0930 9.30
## 3 M1A007B 1 I 0.907 90.7
## 4 M1A007F 1 I 0.000241 0.024
## 5 M1A007F 1 C 0.393 39.3
## 6 M1A007F 1 L 0.607 60.7
## 7 M1A008D 1 C 0 0
## 8 M1A008D 1 L 0.0935 9.35
## 9 M1A008D 1 I 0.907 90.7
## 10 M1A020B 1 C 0 0
## # ℹ 164 more rows
marginal_summary <- indiv_posteriors_marginal %>%
pivot_longer(cols = !subject_id & !episode_number,
names_to = "posterior_type",
values_to = "posterior_value") %>%
mutate(posterior_classification = case_when(posterior_type == "Posterior_marginal_prob_C" ~ "Recrudescence",
posterior_type == "Posterior_marginal_prob_L" ~ "Relapse",
posterior_type == "Posterior_marginal_prob_I" ~ "Reinfection"),
posterior_classification = factor(posterior_classification,
levels = c("Relapse", "Recrudescence", "Reinfection"))) %>%
select(-posterior_type)
marginal_summary
## # A tibble: 141 × 4
## subject_id episode_number posterior_value posterior_classification
## <chr> <int> <dbl> <fct>
## 1 M1A008D 2 0 Recrudescence
## 2 M1A008D 2 0.0935 Relapse
## 3 M1A008D 2 0.907 Reinfection
## 4 M1A083D 2 0 Recrudescence
## 5 M1A083D 2 0.190 Relapse
## 6 M1A083D 2 0.810 Reinfection
## 7 M1A007B 2 0 Recrudescence
## 8 M1A007B 2 0.0930 Relapse
## 9 M1A007B 2 0.907 Reinfection
## 10 M1A007F 2 0.393 Recrudescence
## # ℹ 131 more rows
marginal_summary %>%
group_by(subject_id) %>%
arrange(desc(posterior_value), .by_group = TRUE) %>%
mutate(subject_id = factor(subject_id, levels = unique(subject_id[order(posterior_value, decreasing = TRUE)]))) %>%
ggplot(aes(x = factor(episode_number), y = posterior_value, group = episode_number, fill = posterior_classification)) +
geom_bar(stat = "identity", position = "fill") +
scale_fill_manual(values = c("Relapse" = "turquoise3",
"Recrudescence" = "skyblue4",
"Reinfection" = "magenta3")) +
scale_x_discrete(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0)) +
labs(x = "Episode number",
y = "Posterior probability",
fill = "") +
theme_bw() +
facet_wrap(~subject_id)
### By community
marginal_summary %>%
left_join(analysis_data %>% select(subject_id, community) %>% distinct(),
by = "subject_id") %>%
group_by(subject_id) %>%
arrange(desc(posterior_value), .by_group = TRUE) %>%
mutate(subject_id = factor(subject_id, levels = unique(subject_id[order(posterior_value, decreasing = TRUE)]))) %>%
ggplot(aes(x = factor(episode_number), y = posterior_value, group = episode_number, fill = posterior_classification)) +
geom_bar(stat = "identity", position = "fill") +
scale_fill_manual(values = c("Relapse" = "turquoise3",
"Recrudescence" = "skyblue4",
"Reinfection" = "magenta3")) +
scale_x_discrete(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0)) +
labs(x = "Episode number",
y = "Posterior probability",
fill = "") +
theme_bw() +
facet_wrap(community ~ subject_id)
### By days since first episode during study period
marginal_summary %>%
left_join(analysis_data %>% select(subject_id, episode_number, community, visit_date, days_since_enrolment, days_since_last_episode) %>% distinct(),
by = c("subject_id", "episode_number")) %>%
group_by(subject_id) %>%
arrange(desc(posterior_value), .by_group = TRUE) %>%
mutate(subject_id = factor(subject_id, levels = unique(subject_id[order(posterior_value, decreasing = TRUE)]))) %>%
ggplot(aes(x = days_since_enrolment, y = posterior_value, group = episode_number, fill = posterior_classification)) +
geom_bar(stat = "identity", position = "fill", width = 10) +
scale_fill_manual(values = c("Relapse" = "turquoise3",
"Recrudescence" = "skyblue4",
"Reinfection" = "magenta3")) +
scale_x_continuous(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0)) +
labs(x = "Days since first episode",
y = "Posterior probability",
fill = "") +
theme_bw() +
facet_wrap(~subject_id)
## Plot joint probabilities
pal1 <- c(
"C" = "turquoise3",
"I" = "magenta3",
"L" = "skyblue4")
pal2 <- c(
"II" = "magenta3",
"IL" = "darkorange2",
"LI" = "goldenrod2",
"LL" = "skyblue4",
"CI" = "saddlebrown",
"CL" = "turquoise4"
)
pal3 <- c(
"LLL" = "skyblue4",
"LIL" = "goldenrod3",
"ILL" = "orangered3",
"IIL" = "darkorange3",
"LLI" = "orange3",
"ILI" = "sienna4",
"LII" = "peru",
"III" = "magenta3"
)
combined_pal <- c(pal1, pal2, pal3)
plotJointProb <- function(data, recurrence_n, prob_threshold, color_palette, ...){
data %>%
filter(total_recurrences == recurrence_n, joint_probability > prob_threshold) %>%
group_by(subject_id) %>%
mutate(state_pair = fct_reorder(state_pair, joint_probability)) %>%
ungroup() %>%
ggplot(aes(x = joint_probability,
y = subject_id,
fill = state_pair)) +
geom_bar(position = "stack", stat = "identity") +
scale_x_continuous(expand = c(0, 0)) +
scale_y_discrete(expand = c(0, 0)) +
scale_fill_manual(values = color_palette) +
labs(x = "Joint probability estimate",
y = "Participant",
fill = "Classification state") +
theme_bw() +
facet_wrap(~total_recurrences, scales = "free_y")
}
plot_joint1 <- plotJointProb(joint_summary, 1, 0, pal1)
plot_joint2 <- plotJointProb(joint_summary, 2, 0, pal2) + guides(fill = guide_legend(ncol = 2))
plot_joint3 <- plotJointProb(joint_summary, 3, 0.001, pal3) + guides(fill = guide_legend(ncol = 3))
(plot_joint1 | plot_joint2) / (plot_joint3) +
plot_annotation(subtitle = "I = Reinfection, L = Relapse, C = Recrudescence",
caption = expression(italic("For >2 recurrences, only probability estimates > 0.001 are shown"))) +
plot_layout(heights = c(4, 0.5))
### By community and number of recurrences
joint_summary %>%
left_join(analysis_data %>% select(subject_id, community) %>% distinct(),
by = "subject_id") %>%
filter(joint_probability > 0.001) %>%
group_by(subject_id) %>%
mutate(state_pair = fct_reorder(state_pair, joint_probability)) %>%
ungroup() %>%
ggplot(aes(x = joint_probability,
y = reorder(subject_id, total_recurrences),
fill = state_pair)) +
geom_bar(position = "stack", stat = "identity") +
scale_x_continuous(expand = c(0, 0)) +
scale_y_discrete(expand = c(0, 0)) +
scale_fill_manual(values = combined_pal) +
labs(x = "Joint probability estimate",
y = "Participant",
fill = "Classification state",
subtitle = "I = Reinfection, L = Relapse, C = Recrudescence",
caption = expression(italic("Color coding: if > 1 recurrence, blue shades denote combinations of relapse and recrudescence (L and C), shades of orange/browns denote \ncombos of reinfection and relapse (I and L) and green shades denote combos of reinfection with recrudescence (I and C)"))) +
theme_bw() +
facet_wrap(community~total_recurrences, scales = "free") +
# facet_wrap(total_recurrences~treatment_arm, scales = "free") +
theme(plot.caption = element_text(hjust = 0, vjust = -2))
### ‘Confidence’ in highest joint probability estimate What is the joint
probability estimate for the ‘highest ranking’ state pair
classification? I.e. if >80% relatively high confidence? n=19
joint_summary %>%
filter(joint_probability > 0.001) %>%
group_by(subject_id) %>%
# get the highest prob for each subj and store ranking
mutate(ranking = rank(-joint_probability, ties.method = "first")) %>%
arrange(subject_id, ranking) %>%
filter(ranking == 1) %>%
ggplot(aes(x = reorder(subject_id, -joint_probability),
y = joint_probability,
fill = factor(total_recurrences))) +
geom_col() +
geom_hline(yintercept = 0.8, color = "darkgray", linetype = "dashed") +
labs(x = "Participant",
y = "Joint probability estimate for highest 'ranking' state pair",
fill = "Number of recurrent episodes",
caption = expression(italic("Dashed line indicates an arbitrary threshold of 80%"))) +
theme_bw() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
How many state pairs make up the arbitrary 80% threshold?
joint_summary %>%
filter(joint_probability > 0.001) %>%
group_by(subject_id) %>%
# Get the highest prob for each subject and store ranking
mutate(ranking = rank(-joint_probability, ties.method = "first")) %>%
# reverse ranking order of ranking so we plot the highest rank at the bottom
arrange(subject_id, desc(ranking)) %>%
mutate(ranking = factor(ranking, levels = unique(ranking))) %>%
ggplot(aes(x = subject_id,
y = joint_probability,
fill = ranking)) +
geom_bar(stat = "identity", position = "stack") +
geom_hline(yintercept = 0.8, color = "darkgray", linetype = "dashed") +
scale_fill_brewer(type = "qual", "Paired") +
# scale_fill_manual(values = colorRampPalette(brewer.pal(12, "Paired"))(14),
# guide = guide_legend(reverse = T)) +
scale_x_discrete(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0)) +
labs(x = "Participant",
y = "Joint probability estimate for state pair",
fill = "Number of possible \nestimated state pairs",
caption = expression(italic("Dashed line indicates an arbitrary threshold of 80%"))) +
theme_bw() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
facet_wrap(~total_recurrences, scales = "free_x")
joint_top3_summary <- joint_summary %>%
select(-percentage) %>%
group_by(subject_id) %>%
# Get the highest prob for each subject and store ranking
mutate(ranking = rank(-joint_probability, ties.method = "first")) %>%
arrange(subject_id, ranking) %>%
filter(ranking %in% c(1, 2, 3))
We will keep the ‘highest ranking’ joint probability estimate so we can compare the state classification for each episode with other metrics
joint_recurrence_summary <- joint_top3_summary %>%
select(-total_recurrences) %>%
filter(ranking == 1) %>%
mutate(state_pair_split = strsplit(state_pair, "")) %>% # Split state_pair into individual letters
unnest(state_pair_split) %>% # Unnest to create a new row for each letter
group_by(subject_id) %>%
mutate(
episode_number = row_number() + 1, # Create episode number starting from n+1 to reflect number of recurrence
joint_probability = joint_probability, # Keep the same joint_probability for all episodes\
state_classification = case_when(state_pair_split == "L" ~ "Relapse",
state_pair_split == "C" ~ "Recrudescence",
state_pair_split == "I" ~ "Reinfection")) %>%
select(subject_id, episode_number, state_classification, joint_probability)
joint_recurrence_summary %>%
ggplot(aes(x = factor(episode_number), y = joint_probability, fill = state_classification)) +
geom_col() +
scale_fill_manual(values = c("Relapse" = "turquoise3",
"Recrudescence" = "skyblue4",
"Reinfection" = "magenta3")) +
scale_x_discrete(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0)) +
labs(x = "Episode number",
y = "Posterior joint probability",
fill = "") +
theme_bw() +
facet_wrap(~subject_id)
posterior_summary <- marginal_summary %>%
rename("marginal_posterior_probability" = "posterior_value",
"marginal_classification" = "posterior_classification") %>%
left_join(joint_recurrence_summary %>%
rename("joint_classification" = "state_classification",
"joint_posterior_probability" = "joint_probability"),
by = c("subject_id", "episode_number")) %>%
pivot_longer(cols = c(marginal_posterior_probability,
joint_posterior_probability,
marginal_classification,
joint_classification),
names_to = c("estimate_type", ".value"),
names_pattern = "(marginal|joint)_(posterior_probability|classification)") %>%
distinct()
posterior_summary %>%
group_by(subject_id, episode_number) %>%
arrange(desc(posterior_probability), .by_group = TRUE) %>%
mutate(classification = factor(classification, levels = unique(classification))) %>%
ungroup() %>%
ggplot(aes(x = factor(episode_number), y = posterior_probability, group = episode_number)) +
geom_bar(data = . %>% filter(estimate_type == "marginal"),
aes(fill = classification),
stat = "identity",
position = "fill") +
geom_segment(data = . %>% filter(estimate_type == "joint"),
aes(x = as.numeric(factor(episode_number)) - 0.4,
xend = as.numeric(factor(episode_number)) + 0.4,
y = posterior_probability,
yend = posterior_probability,
color = classification),
size = 1.5) +
geom_point(data = . %>% filter(estimate_type == "joint"),
aes(color = classification,
fill = classification),
shape = 21) +
scale_fill_manual(values = c("Relapse" = "turquoise3",
"Recrudescence" = "skyblue4",
"Reinfection" = "magenta3")) +
scale_color_manual(values = c("Relapse" = "#006064",
"Recrudescence" = "#3B4F75",
"Reinfection" = "#8B008B")) +
scale_x_discrete(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0)) +
labs(x = "Episode number",
y = "Posterior probability",
fill = "Marginal classification",
color = "Joint probability estimate \n and classification") +
theme_bw() +
facet_wrap(~subject_id)
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
peru_pnh <- read.csv(here("data/final", "peru_PNH_data.csv"))
ibd_r_data <- readRDS(here("data/final", "peru_all_meta.rds"))
ibd_rhat_data <- readRDS(here("data/final", "peru_sig_meta.rds"))
For direct comparison with the we can only look at the paired comparisons between baseline samples and ‘follow-up’ recurrent samples, but not other pw comparisons for a given participant.
peru_ibd <- ibd_r_data %>%
# filter to include only paired samples
filter(comparison_type == "paired") %>%
# merge with analysis_data to get patient_name and 'day' info
left_join(analysis_data %>%
select(sample_id, subject_id,
day = days_since_enrolment) %>%
distinct(),
by = c("sampleid1" = "sample_id")) %>%
rename("subject_id1" = "subject_id",
"day_id1" = "day") %>%
# do the same for sample id 2 in pair - merge with analysis_data to get patient_name and 'day' info
left_join(analysis_data %>%
select(sample_id, subject_id,
day = days_since_enrolment) %>%
distinct(),
by = c("sampleid2" = "sample_id")) %>%
rename("day_id2" = "day") %>%
select(-subject_id1) %>%
# filter to include only pw comparisons to first episode in study period
filter(day_id2 == "0") %>%
group_by(subject_id) %>%
arrange(date1) %>%
# create episode_number accounting for baseline as episode 1
mutate(episode_number = row_number() + 1) %>%
ungroup()
classification_summary <- indiv_posteriors_marginal %>%
rename("posterior_probability_recrudescence" = "Posterior_marginal_prob_C",
"posterior_probability_relapse" = "Posterior_marginal_prob_L",
"posterior_probability_reinfection" = "Posterior_marginal_prob_I") %>%
# merge with epi variables
left_join(analysis_data %>% select(subject_id, sample_id, episode_number, community, visit_date, days_since_enrolment, days_since_last_episode) %>% distinct(),
by = c("subject_id", "episode_number")) %>%
# merge with PNH data
left_join(peru_pnh,
by = c("subject_id" = "Patient",
"episode_number" = "recurrence")) %>%
rename("prop_same_haps_1minusPNH" = "Prop_het",
"pnh_range" = "identity",
"classification" = "Classification") %>%
# create bins for 1-PNH (IBS metric)
mutate(ibs_range = cut(prop_same_haps_1minusPNH,
breaks = c(0, 0.25, 0.5, 0.75, 1),
include.lowest = TRUE,
labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1"))) %>%
# merge with IBD data
left_join(peru_ibd,
by = c("subject_id", "episode_number")) %>%
# create bins for IBD
mutate(ibd_range = cut(estimate,
breaks = c(0, 0.25, 0.5, 0.75, 1),
include.lowest = TRUE,
labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1"))) %>%
# merge with joint probabilities
left_join(joint_recurrence_summary %>%
rename("joint_classification" = "state_classification"),
by = c("subject_id", "episode_number"))
classification_summary
## subject_id episode_number posterior_probability_recrudescence
## 1 M1A008D 2 0.0000000
## 2 M1A083D 2 0.0000000
## 3 M1A007B 2 0.0000000
## 4 M1A007F 2 0.3925671
## 5 M1B020F 2 0.0000000
## 6 M1B040B 2 0.0000000
## 7 M1A085C 2 0.0000000
## 8 M1A161C 2 0.0000000
## 9 M1A082B 2 0.0000000
## 10 M1A020B 2 0.0000000
## 11 M1B011B 2 0.0000000
## 12 M1A043E 2 0.0000000
## 13 M1D049D 2 0.0000000
## 14 M1D049D 3 0.0000000
## 15 M1C019D 2 0.0000000
## 16 M1D042A 2 0.0000000
## 17 M1C081A 2 0.0000000
## 18 M1C081A 3 0.0000000
## 19 M1C081A 4 0.0000000
## 20 M1C002A 2 0.0000000
## 21 M1C061B 2 0.0000000
## 22 M1D010A 2 0.0000000
## 23 M1D024B 2 0.0000000
## 24 M1D026A 2 0.0000000
## 25 M1D026A 3 0.0000000
## 26 M1E044C 2 0.0000000
## 27 M1E044C 3 0.0000000
## 28 M1D019A 2 0.0000000
## 29 M1C007E 2 0.0000000
## 30 M1D026D 2 0.0000000
## 31 M1D026D 3 0.0000000
## 32 M1E016A 2 0.0000000
## 33 M1E025B 2 0.0000000
## 34 M1D036C 2 0.0000000
## 35 M1D049A 2 0.3050571
## 36 M1E036B 2 0.3208016
## 37 M1E036B 3 0.0000000
## 38 M1E026C 2 0.3776514
## 39 M1E028C 2 0.0000000
## 40 M1C031G 2 0.0000000
## 41 M1E042B 2 0.0000000
## 42 M1E022A 2 0.0000000
## 43 M1D038D 2 0.0000000
## 44 M1C010B 2 0.0000000
## 45 M1D036A 2 0.3846383
## 46 M1C090C 2 0.0000000
## 47 M1D006D 2 0.0000000
## posterior_probability_relapse posterior_probability_reinfection sample_id
## 1 0.09349443 0.9065055681 AC032502
## 2 0.19037917 0.8096208306 AC030871
## 3 0.09303113 0.9069688689 AC032493
## 4 0.60719228 0.0002405764 AC032497
## 5 0.21863282 0.7813671776 AC036291
## 6 0.09304682 0.9069531828 AC035178
## 7 0.25947486 0.7405251391 AC035159
## 8 0.18211923 0.8178807686 AC031719
## 9 0.22280060 0.7771994031 AC035333
## 10 0.99832075 0.0016792496 AC033642
## 11 0.99848175 0.0015182461 AC034529
## 12 0.99689703 0.0031029707 AC034279
## 13 0.06188471 0.9381152932 AL025765
## 14 0.55668424 0.4433157594 AL028294
## 15 0.19383355 0.8061664541 AL030931
## 16 0.25135792 0.7486420841 AL030541
## 17 0.34132835 0.6586716540 AL031062
## 18 0.34705495 0.6529450466 AL031862
## 19 0.14937003 0.8506299712 AL032411
## 20 0.68892955 0.3110704468 AL026528
## 21 0.19107623 0.8089237659 AL030237
## 22 0.28365892 0.7163410818 AL026640
## 23 0.15492601 0.8450739850 AL032765
## 24 0.18504804 0.8149519614 AL026884
## 25 0.05134035 0.9486596501 AL031366
## 26 0.09454487 0.9054551295 AL028375
## 27 0.03554051 0.9644594850 AL030634
## 28 0.16635935 0.8336406488 AL027673
## 29 0.33769327 0.6623067329 AL030073
## 30 0.27937051 0.7206294866 AL026647
## 31 0.03841057 0.9615894348 AL032236
## 32 0.20790984 0.7920901638 AL030022
## 33 0.09322659 0.9067734120 AL032744
## 34 0.09302329 0.9069767081 AL032119
## 35 0.69142943 0.0035134297 AL027682
## 36 0.67770238 0.0014959905 AL028365
## 37 0.07543176 0.9245682361 AL030767
## 38 0.61812147 0.0042271206 AL028964
## 39 0.55039121 0.4496087949 AL032340
## 40 0.09302326 0.9069767394 AL033228
## 41 0.28093657 0.7190634262 AL030500
## 42 0.09302326 0.9069767442 AL032500
## 43 0.14477166 0.8552283441 AL033029
## 44 0.14798465 0.8520153488 AL032912
## 45 0.61520379 0.0001579491 AL032118
## 46 0.36994567 0.6300543307 AL033098
## 47 0.54277193 0.4572280717 AL033182
## community visit_date days_since_enrolment days_since_last_episode sample
## 1 Cahuide 2015-08-03 244 244 AC032502
## 2 Cahuide 2015-06-02 182 182 AC030871
## 3 Cahuide 2015-08-03 243 243 AC032493
## 4 Cahuide 2015-08-03 182 182 AC032497
## 5 Cahuide 2015-12-07 279 279 AC036291
## 6 Cahuide 2015-11-03 236 236 AC035178
## 7 Cahuide 2015-11-02 178 178 AC035159
## 8 Cahuide 2015-07-06 53 53 AC031719
## 9 Cahuide 2015-11-04 149 149 AC035333
## 10 Cahuide 2015-09-04 28 28 AC033642
## 11 Cahuide 2015-10-07 58 58 AC034529
## 12 Cahuide 2015-10-05 34 34 AC034279
## 13 Lupuna 2015-03-02 87 87 AL025765
## 14 Lupuna 2015-06-03 180 93 AL028294
## 15 Lupuna 2015-09-07 244 244 AL030931
## 16 Lupuna 2015-09-01 232 232 AL030541
## 17 Lupuna 2015-09-09 240 240 AL031062
## 18 Lupuna 2015-10-19 280 40 AL031862
## 19 Lupuna 2015-11-06 298 18 AL032411
## 20 Lupuna 2015-04-06 83 83 AL026528
## 21 Lupuna 2015-08-10 209 209 AL030237
## 22 Lupuna 2015-04-07 83 83 AL026640
## 23 Lupuna 2015-12-02 322 322 AL032765
## 24 Lupuna 2015-04-09 65 65 AL026884
## 25 Lupuna 2015-10-06 245 180 AL031366
## 26 Lupuna 2015-06-04 119 119 AL028375
## 27 Lupuna 2015-09-04 211 92 AL030634
## 28 Lupuna 2015-05-07 90 90 AL027673
## 29 Lupuna 2015-08-06 177 177 AL030073
## 30 Lupuna 2015-04-07 31 31 AL026647
## 31 Lupuna 2015-11-04 242 211 AL032236
## 32 Lupuna 2015-08-06 122 122 AL030022
## 33 Lupuna 2015-12-02 239 239 AL032744
## 34 Lupuna 2015-11-03 210 210 AL032119
## 35 Lupuna 2015-05-07 30 30 AL027682
## 36 Lupuna 2015-06-04 57 57 AL028365
## 37 Lupuna 2015-09-05 150 93 AL030767
## 38 Lupuna 2015-07-06 63 63 AL028964
## 39 Lupuna 2015-11-06 178 178 AL032340
## 40 Lupuna 2015-12-15 194 194 AL033228
## 41 Lupuna 2015-09-01 57 57 AL030500
## 42 Lupuna 2015-11-10 126 126 AL032500
## 43 Lupuna 2015-12-07 153 153 AL033029
## 44 Lupuna 2015-12-04 116 116 AL032912
## 45 Lupuna 2015-11-03 57 57 AL032118
## 46 Lupuna 2015-12-09 93 93 AL033098
## 47 Lupuna 2015-12-11 15 15 AL033182
## date treatment prop_same_haps_1minusPNH delay_since_prev_ep
## 1 2015-08-03 No treatment 0.5697270 244
## 2 2015-06-02 No treatment 0.4233232 182
## 3 2015-08-03 No treatment 0.2448705 243
## 4 2015-08-03 No treatment 1.0000000 182
## 5 2015-12-07 No treatment 0.5387873 279
## 6 2015-11-03 No treatment 0.2181635 236
## 7 2015-11-02 No treatment 0.1912069 178
## 8 2015-07-06 No treatment 0.2070444 53
## 9 2015-11-04 No treatment 0.4629275 149
## 10 2015-09-04 No treatment 1.0000000 28
## 11 2015-10-07 No treatment 1.0000000 58
## 12 2015-10-05 No treatment 1.0000000 34
## 13 2015-03-02 No treatment 0.4191986 87
## 14 2015-06-03 No treatment 1.0000000 180
## 15 2015-09-07 No treatment 0.5408380 244
## 16 2015-09-01 No treatment 0.1166691 232
## 17 2015-09-09 No treatment 0.6867761 240
## 18 2015-10-19 No treatment 0.4999684 280
## 19 2015-11-06 No treatment 0.4800970 298
## 20 2015-04-06 No treatment 0.7524227 83
## 21 2015-08-10 No treatment 0.4226612 209
## 22 2015-04-07 No treatment 0.1819739 83
## 23 2015-12-02 No treatment 0.4806984 322
## 24 2015-04-09 No treatment 0.2190177 65
## 25 2015-10-06 No treatment 0.3863808 245
## 26 2015-06-04 No treatment 0.0000000 119
## 27 2015-09-04 No treatment 0.3979616 211
## 28 2015-05-07 No treatment 0.7902418 90
## 29 2015-08-06 No treatment 0.4848159 177
## 30 2015-04-07 No treatment 0.7154368 31
## 31 2015-11-04 No treatment 0.4973695 242
## 32 2015-08-06 No treatment 0.4893551 122
## 33 2015-12-02 No treatment 0.4341896 239
## 34 2015-11-03 No treatment 0.2948058 210
## 35 2015-05-07 No treatment 1.0000000 30
## 36 2015-06-04 No treatment 1.0000000 57
## 37 2015-09-05 No treatment 0.2331090 150
## 38 2015-07-06 No treatment 1.0000000 63
## 39 2015-11-06 No treatment 0.6584768 178
## 40 2015-12-15 No treatment 0.1301714 194
## 41 2015-09-01 No treatment 0.3045193 57
## 42 2015-11-10 No treatment 0.0000000 126
## 43 2015-12-07 No treatment 0.2248418 153
## 44 2015-12-04 No treatment 0.1401421 116
## 45 2015-11-03 No treatment 1.0000000 57
## 46 2015-12-09 No treatment 0.8922594 93
## 47 2015-12-11 No treatment 0.3437509 15
## change_moi max_moi clones pnh_range classification .group ibs_range
## 1 0 2 poly (0,0.75] Homologous 3 0.5-0.75
## 2 1 2 poly (0,0.75] Homologous 7 0.25-0.5
## 3 0 2 poly (0.75,1] Heterologous 1 0-0.25
## 4 0 1 mono (-Inf,0] Homologous 2 0.75-1
## 5 -1 1 mono (0,0.75] Homologous 11 0.5-0.75
## 6 0 2 poly (0.75,1] Heterologous 12 0-0.25
## 7 0 1 mono (0.75,1] Heterologous 8 0-0.25
## 8 1 2 poly (0.75,1] Heterologous 9 0-0.25
## 9 -1 1 mono (0,0.75] Homologous 6 0.25-0.5
## 10 0 2 poly (-Inf,0] Homologous 4 0.75-1
## 11 1 2 poly (-Inf,0] Homologous 10 0.75-1
## 12 0 2 poly (-Inf,0] Homologous 5 0.75-1
## 13 -1 2 poly (0,0.75] Homologous 32 0.25-0.5
## 14 -1 1 mono (-Inf,0] Homologous 32 0.75-1
## 15 1 2 poly (0,0.75] Homologous 16 0.5-0.75
## 16 0 1 mono (0.75,1] Heterologous 30 0-0.25
## 17 2 3 poly (0,0.75] Homologous 19 0.5-0.75
## 18 -1 2 poly (0,0.75] Homologous 19 0.25-0.5
## 19 -1 1 mono (0,0.75] Homologous 19 0.25-0.5
## 20 0 1 mono (0,0.75] Homologous 13 0.75-1
## 21 -1 1 mono (0,0.75] Homologous 18 0.25-0.5
## 22 0 1 mono (0.75,1] Heterologous 22 0-0.25
## 23 2 3 poly (0,0.75] Homologous 24 0.25-0.5
## 24 -1 1 mono (0.75,1] Heterologous 25 0-0.25
## 25 1 2 poly (0,0.75] Homologous 25 0.25-0.5
## 26 0 2 poly (0.75,1] Heterologous 40 0-0.25
## 27 0 2 poly (0,0.75] Homologous 40 0.25-0.5
## 28 0 2 poly (0,0.75] Homologous 23 0.75-1
## 29 0 1 mono (0,0.75] Homologous 14 0.25-0.5
## 30 -2 1 mono (0,0.75] Homologous 26 0.5-0.75
## 31 1 2 poly (0,0.75] Homologous 26 0.25-0.5
## 32 -1 1 mono (0,0.75] Homologous 33 0.25-0.5
## 33 0 2 poly (0,0.75] Homologous 35 0.25-0.5
## 34 0 2 poly (0,0.75] Homologous 28 0.25-0.5
## 35 -1 1 mono (-Inf,0] Homologous 31 0.75-1
## 36 -1 1 mono (-Inf,0] Homologous 38 0.75-1
## 37 1 2 poly (0.75,1] Heterologous 38 0-0.25
## 38 0 1 mono (-Inf,0] Homologous 36 0.75-1
## 39 0 1 mono (0,0.75] Homologous 37 0.5-0.75
## 40 0 2 poly (0.75,1] Heterologous 17 0-0.25
## 41 0 1 mono (0,0.75] Homologous 39 0.25-0.5
## 42 0 2 poly (0.75,1] Heterologous 34 0-0.25
## 43 -2 1 mono (0.75,1] Heterologous 29 0-0.25
## 44 2 3 poly (0.75,1] Heterologous 15 0-0.25
## 45 0 1 mono (-Inf,0] Homologous 27 0.75-1
## 46 1 3 poly (0,0.75] Homologous 20 0.75-1
## 47 0 1 mono (0,0.75] Homologous 21 0.25-0.5
## sampleid1 sampleid2 estimate p_value CI_lower CI_upper patientid1
## 1 AC032502 AC025519 0.000 5.000000e-01 0.000 0.559 A008D
## 2 AC030871 AC025540 0.000 5.000000e-01 0.000 0.486 A083D
## 3 AC032493 AC025640 0.000 5.000000e-01 0.000 0.259 A007B
## 4 AC032497 AC027188 1.000 1.238651e-05 0.704 1.000 A007F
## 5 AC036291 AC028231 0.002 4.975801e-01 0.000 0.618 B020F
## 6 AC035178 AC028730 0.000 5.000000e-01 0.000 0.266 B040B
## 7 AC035159 AC030405 0.000 5.000000e-01 0.000 0.368 A085C
## 8 AC031719 AC030629 0.000 5.000000e-01 0.000 0.251 A6C
## 9 AC035333 AC031312 0.208 1.561044e-01 0.000 0.639 A082B
## 10 AC033642 AC032947 1.000 4.389883e-05 0.624 1.000 A020B
## 11 AC034529 AC033016 1.000 7.266956e-05 0.652 1.000 B0B
## 12 AC034279 AC033409 1.000 5.540812e-04 0.504 1.000 A043E
## 13 AL025765 AL023661 0.000 5.000000e-01 0.000 0.391 D049D
## 14 AL028294 AL023661 1.000 9.766689e-02 0.000 1.000 D049D
## 15 AL030931 AL024180 0.063 4.234904e-01 0.000 0.640 C09D
## 16 AL030541 AL024531 0.000 5.000000e-01 0.000 0.247 D042A
## 17 AL031062 AL024563 0.285 1.688367e-01 0.000 0.784 C08A
## 18 AL031862 AL024563 0.150 2.620348e-01 0.000 0.616 C08A
## 19 AL032411 AL024563 0.152 3.276786e-01 0.000 0.734 C08A
## 20 AL026528 AL024616 0.574 3.059022e-02 0.000 0.921 C002A
## 21 AL030237 AL024635 0.000 5.000000e-01 0.000 0.636 C06B
## 22 AL026640 AL024651 0.020 4.624243e-01 0.000 0.544 D00A
## 23 AL032765 AL024656 0.000 5.000000e-01 0.000 0.406 D024B
## 24 AL026884 AL025075 0.000 5.000000e-01 0.000 0.329 D026A
## 25 AL031366 AL025075 0.000 5.000000e-01 0.000 0.489 D026A
## 26 AL028375 AL025230 0.000 5.000000e-01 0.000 0.213 E044C
## 27 AL030634 AL025230 0.000 5.000000e-01 0.000 0.609 E044C
## 28 AL027673 AL025336 0.401 1.248052e-01 0.000 0.880 D09A
## 29 AL030073 AL025515 0.145 3.029587e-01 0.000 0.667 C007E
## 30 AL026647 AL026178 0.090 4.170238e-01 0.000 0.793 D026D
## 31 AL032236 AL026178 0.000 5.000000e-01 0.000 0.439 D026D
## 32 AL030022 AL026486 0.000 5.000000e-01 0.000 0.587 E06A
## 33 AL032744 AL026609 0.044 4.057350e-01 0.000 0.510 E025B
## 34 AL032119 AL026649 0.000 5.000000e-01 0.000 0.275 D036C
## 35 AL027682 AL026659 1.000 2.282026e-04 0.584 1.000 D049A
## 36 AL028365 AL026738 1.000 9.728660e-05 0.603 1.000 E036B
## 37 AL030767 AL026738 0.000 5.000000e-01 0.000 0.223 E036B
## 38 AL028964 AL027357 1.000 2.553009e-04 0.594 1.000 E026C
## 39 AL032340 AL027877 0.423 7.555122e-02 0.000 0.836 E028C
## 40 AL033228 AL028427 0.000 5.000000e-01 0.000 0.313 C03G
## 41 AL030500 AL028971 0.046 4.114015e-01 0.000 0.526 E042B
## 42 AL032500 AL029017 0.000 5.000000e-01 0.000 0.213 E022A
## 43 AL033029 AL029071 0.000 5.000000e-01 0.000 0.276 D038D
## 44 AL032912 AL030230 0.000 5.000000e-01 0.000 0.311 C00B
## 45 AL032118 AL030912 1.000 7.854532e-06 0.624 1.000 D036A
## 46 AL033098 AL030953 0.574 8.319682e-02 0.000 0.972 C090C
## 47 AL033182 AL032715 0.308 6.414419e-02 0.000 0.770 D006D
## patientid2 date1 date2 comparison_type pair
## 1 A008D 2015-08-03 2014-12-02 paired AC032502 / AC025519
## 2 A083D 2015-06-02 2014-12-02 paired AC030871 / AC025540
## 3 A007B 2015-08-03 2014-12-03 paired AC032493 / AC025640
## 4 A007F 2015-08-03 2015-02-02 paired AC032497 / AC027188
## 5 B020F 2015-12-07 2015-03-03 paired AC036291 / AC028231
## 6 B040B 2015-11-03 2015-03-12 paired AC035178 / AC028730
## 7 A085C 2015-11-02 2015-05-08 paired AC035159 / AC030405
## 8 A6C 2015-07-06 2015-05-14 paired AC031719 / AC030629
## 9 A082B 2015-11-04 2015-06-08 paired AC035333 / AC031312
## 10 A020B 2015-09-04 2015-08-07 paired AC033642 / AC032947
## 11 B0B 2015-10-07 2015-08-10 paired AC034529 / AC033016
## 12 A043E 2015-10-05 2015-09-01 paired AC034279 / AC033409
## 13 D049D 2015-03-02 2014-12-05 paired AL025765 / AL023661
## 14 D049D 2015-06-03 2014-12-05 paired AL028294 / AL023661
## 15 C09D 2015-09-07 2015-01-06 paired AL030931 / AL024180
## 16 D042A 2015-09-01 2015-01-12 paired AL030541 / AL024531
## 17 C08A 2015-09-09 2015-01-12 paired AL031062 / AL024563
## 18 C08A 2015-10-19 2015-01-12 paired AL031862 / AL024563
## 19 C08A 2015-11-06 2015-01-12 paired AL032411 / AL024563
## 20 C002A 2015-04-06 2015-01-13 paired AL026528 / AL024616
## 21 C06B 2015-08-10 2015-01-13 paired AL030237 / AL024635
## 22 D00A 2015-04-07 2015-01-14 paired AL026640 / AL024651
## 23 D024B 2015-12-02 2015-01-14 paired AL032765 / AL024656
## 24 D026A 2015-04-09 2015-02-03 paired AL026884 / AL025075
## 25 D026A 2015-10-06 2015-02-03 paired AL031366 / AL025075
## 26 E044C 2015-06-04 2015-02-05 paired AL028375 / AL025230
## 27 E044C 2015-09-04 2015-02-05 paired AL030634 / AL025230
## 28 D09A 2015-05-07 2015-02-06 paired AL027673 / AL025336
## 29 C007E 2015-08-06 2015-02-10 paired AL030073 / AL025515
## 30 D026D 2015-04-07 2015-03-07 paired AL026647 / AL026178
## 31 D026D 2015-11-04 2015-03-07 paired AL032236 / AL026178
## 32 E06A 2015-08-06 2015-04-06 paired AL030022 / AL026486
## 33 E025B 2015-12-02 2015-04-07 paired AL032744 / AL026609
## 34 D036C 2015-11-03 2015-04-07 paired AL032119 / AL026649
## 35 D049A 2015-05-07 2015-04-07 paired AL027682 / AL026659
## 36 E036B 2015-06-04 2015-04-08 paired AL028365 / AL026738
## 37 E036B 2015-09-05 2015-04-08 paired AL030767 / AL026738
## 38 E026C 2015-07-06 2015-05-04 paired AL028964 / AL027357
## 39 E028C 2015-11-06 2015-05-12 paired AL032340 / AL027877
## 40 C03G 2015-12-15 2015-06-04 paired AL033228 / AL028427
## 41 E042B 2015-09-01 2015-07-06 paired AL030500 / AL028971
## 42 E022A 2015-11-10 2015-07-07 paired AL032500 / AL029017
## 43 D038D 2015-12-07 2015-07-07 paired AL033029 / AL029071
## 44 C00B 2015-12-04 2015-08-10 paired AL032912 / AL030230
## 45 D036A 2015-11-03 2015-09-07 paired AL032118 / AL030912
## 46 C090C 2015-12-09 2015-09-07 paired AL033098 / AL030953
## 47 D006D 2015-12-11 2015-11-26 paired AL033182 / AL032715
## sig_est day_id1 day_id2 ibd_range joint_classification
## 1 not significant 244 0 0-0.25 Reinfection
## 2 not significant 182 0 0-0.25 Reinfection
## 3 not significant 243 0 0-0.25 Reinfection
## 4 significant 182 0 0.75-1 Relapse
## 5 not significant 279 0 0-0.25 Reinfection
## 6 not significant 236 0 0-0.25 Reinfection
## 7 not significant 178 0 0-0.25 Reinfection
## 8 not significant 53 0 0-0.25 Reinfection
## 9 not significant 149 0 0-0.25 Reinfection
## 10 significant 28 0 0.75-1 Relapse
## 11 significant 58 0 0.75-1 Relapse
## 12 significant 34 0 0.75-1 Relapse
## 13 not significant 87 0 0-0.25 Reinfection
## 14 not significant 180 0 0.75-1 Relapse
## 15 not significant 244 0 0-0.25 Reinfection
## 16 not significant 232 0 0-0.25 Reinfection
## 17 not significant 240 0 0.25-0.5 Reinfection
## 18 not significant 280 0 0-0.25 Reinfection
## 19 not significant 298 0 0-0.25 Reinfection
## 20 significant 83 0 0.5-0.75 Relapse
## 21 not significant 209 0 0-0.25 Reinfection
## 22 not significant 83 0 0-0.25 Reinfection
## 23 not significant 322 0 0-0.25 Reinfection
## 24 not significant 65 0 0-0.25 Reinfection
## 25 not significant 245 0 0-0.25 Reinfection
## 26 not significant 119 0 0-0.25 Reinfection
## 27 not significant 211 0 0-0.25 Reinfection
## 28 not significant 90 0 0.25-0.5 Reinfection
## 29 not significant 177 0 0-0.25 Reinfection
## 30 not significant 31 0 0-0.25 Reinfection
## 31 not significant 242 0 0-0.25 Reinfection
## 32 not significant 122 0 0-0.25 Reinfection
## 33 not significant 239 0 0-0.25 Reinfection
## 34 not significant 210 0 0-0.25 Reinfection
## 35 significant 30 0 0.75-1 Relapse
## 36 significant 57 0 0.75-1 Relapse
## 37 not significant 150 0 0-0.25 Reinfection
## 38 significant 63 0 0.75-1 Relapse
## 39 not significant 178 0 0.25-0.5 Relapse
## 40 not significant 194 0 0-0.25 Reinfection
## 41 not significant 57 0 0-0.25 Reinfection
## 42 not significant 126 0 0-0.25 Reinfection
## 43 not significant 153 0 0-0.25 Reinfection
## 44 not significant 116 0 0-0.25 Reinfection
## 45 significant 57 0 0.75-1 Relapse
## 46 not significant 93 0 0.5-0.75 Reinfection
## 47 not significant 15 0 0.25-0.5 Relapse
## joint_probability
## 1 0.9065056
## 2 0.8096208
## 3 0.9069689
## 4 0.6071923
## 5 0.7813672
## 6 0.9069532
## 7 0.7405251
## 8 0.8178808
## 9 0.7771994
## 10 0.9983208
## 11 0.9984818
## 12 0.9968970
## 13 0.5191654
## 14 0.5191654
## 15 0.8061665
## 16 0.7486421
## 17 0.3582473
## 18 0.3582473
## 19 0.3582473
## 20 0.6889296
## 21 0.8089238
## 22 0.7163411
## 23 0.8450740
## 24 0.7761753
## 25 0.7761753
## 26 0.8747423
## 27 0.8747423
## 28 0.8336406
## 29 0.6623067
## 30 0.6951545
## 31 0.6951545
## 32 0.7920902
## 33 0.9067734
## 34 0.9069767
## 35 0.6914294
## 36 0.6321839
## 37 0.6321839
## 38 0.6181215
## 39 0.5503912
## 40 0.9069767
## 41 0.7190634
## 42 0.9069767
## 43 0.8552283
## 44 0.8520153
## 45 0.6152038
## 46 0.6300543
## 47 0.5427719
classification_summary %>%
select(subject_id, sample_id, episode_number, visit_date, days_since_enrolment, days_since_last_episode, starts_with("posterior_"), prop_same_haps_1minusPNH, pnh_range, ibs_range, classification) %>%
pivot_longer(cols = (starts_with("posterior")),
names_to = "posterior_type",
values_to = "posterior_value") %>%
mutate(posterior_range = cut(posterior_value,
breaks = c(0, 0.25, 0.5, 0.75, 1),
include.lowest = TRUE,
labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1"))) %>%
count(ibs_range, classification, posterior_type, posterior_range)
classification_summary %>%
select(subject_id, sample_id, pair, episode_number, visit_date, days_since_enrolment, days_since_last_episode, starts_with("posterior_"), prop_same_haps_1minusPNH, pnh_range, ibs_range, classification) %>%
pivot_longer(cols = (starts_with("posterior")),
names_to = "posterior_type",
values_to = "posterior_value") %>%
mutate(posterior_range = cut(posterior_value,
breaks = c(0, 0.25, 0.5, 0.75, 1),
include.lowest = TRUE,
labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1"))) %>%
ggplot(aes(x = reorder(pair, episode_number), y = posterior_value, group = episode_number, fill = posterior_type)) +
geom_bar(stat = "identity", position = "fill") +
scale_fill_manual(values = c("turquoise3", "magenta3", "skyblue4"),
labels = c("posterior_probability_relapse" = "Relapse",
"posterior_probability_recrudescence" = "Recrudescence",
"posterior_probability_reinfection" = "Reinfection")) +
scale_x_discrete(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0)) +
labs(x = "Sample",
y = "Posterior probability",
fill = "") +
theme_bw() +
facet_wrap(~ibs_range, scales = "free_x") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
### Plot IBS range vs probabilistic classification (joint probability,
highest ranking)
classification_summary %>%
select(subject_id, sample_id, pair, episode_number, visit_date, days_since_enrolment, days_since_last_episode, starts_with("joint"), prop_same_haps_1minusPNH, pnh_range, ibs_range, classification) %>%
ggplot(aes(x = reorder(pair, episode_number), y = joint_probability, group = episode_number, fill = joint_classification)) +
geom_bar(stat = "identity") +
scale_fill_manual(values = c("Relapse" = "turquoise3",
"Recrudescence" = "skyblue4",
"Reinfection" = "magenta3")) +
scale_x_discrete(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0)) +
labs(x = "Sample pair",
y = "Posterior joint probability (highest ranking classification)",
fill = "") +
theme_bw() +
facet_wrap(~ibs_range, scales = "free_x") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
classification_summary %>%
select(subject_id, sample_id, episode_number, visit_date, days_since_enrolment, days_since_last_episode, starts_with("posterior_"), ibd_range, CI_lower, CI_upper, sig_est) %>%
pivot_longer(cols = (starts_with("posterior")),
names_to = "posterior_type",
values_to = "posterior_value") %>%
mutate(posterior_range = cut(posterior_value,
breaks = c(0, 0.25, 0.5, 0.75, 1),
include.lowest = TRUE,
labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1"))) %>%
count(ibd_range, sig_est, posterior_type, posterior_range)
ibd_r_data %>%
filter(comparison_type == "paired") %>%
ggplot(aes(x = pair, y = estimate, group = patientid1, color = sig_est)) +
geom_point(size = 3) +
geom_errorbar(aes(ymin = CI_lower, ymax = CI_upper)) +
scale_color_manual(values = c("not significant" = "darkgrey",
"significant" = "indianred3")) +
facet_wrap(~patientid1, scales = "free_x") +
theme_bw() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
classification_summary %>%
ggplot(aes(x = reorder(sampleid1, days_since_enrolment), y = estimate, group = subject_id, color = sig_est)) +
geom_point(size = 3) +
geom_errorbar(aes(ymin = CI_lower, ymax = CI_upper)) +
scale_color_manual(values = c("not significant" = "darkgrey",
"significant" = "indianred3")) +
labs(x = "Sample",
y = "IBD",
color = "Significance") +
facet_wrap(~subject_id, scales = "free_x") +
theme_bw() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
ibd_r_data %>%
filter(comparison_type == "not paired") %>%
ggplot(aes(x = pair, y = estimate, group = patientid1, color = sig_est)) +
geom_point(size = 3) +
geom_errorbar(aes(ymin = CI_lower, ymax = CI_upper)) +
scale_color_manual(values = c("not significant" = "darkgrey",
"significant" = "indianred3")) +
theme_bw() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
Perhaps reinfections are more likely to occur after more time?
classification_summary %>%
ggplot(aes(x = days_since_last_episode, y = estimate, group = subject_id, color = sig_est)) +
geom_point(size = 3) +
geom_errorbar(aes(ymin = CI_lower, ymax = CI_upper)) +
scale_color_manual(values = c("not significant" = "darkgrey",
"significant" = "indianred3")) +
labs(x = "Days since last episode",
y = "IBD",
fill = "Significance") +
facet_wrap(~subject_id, scales = "free_x") +
theme_bw() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
classification_summary %>%
select(subject_id, sample_id, pair, episode_number, visit_date, days_since_enrolment, days_since_last_episode, starts_with("posterior_"), ibd_range, CI_lower, CI_upper, sig_est) %>%
pivot_longer(cols = (starts_with("posterior")),
names_to = "posterior_type",
values_to = "posterior_value") %>%
mutate(posterior_range = cut(posterior_value,
breaks = c(0, 0.25, 0.5, 0.75, 1),
include.lowest = TRUE,
labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1"))) %>%
ggplot(aes(x = reorder(pair, episode_number), y = posterior_value, group = episode_number, fill = posterior_type)) +
geom_bar(stat = "identity", position = "fill") +
scale_fill_manual(values = c("turquoise3", "magenta3", "skyblue4"),
labels = c("posterior_probability_relapse" = "Relapse",
"posterior_probability_recrudescence" = "Recrudescence",
"posterior_probability_reinfection" = "Reinfection")) +
labs(x = "Sample pair",
y = "Posterior probability",
fill = "") +
theme_bw() +
facet_wrap(~ibd_range, scales = "free_x") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
classification_summary %>%
select(subject_id, sample_id, pair, episode_number, visit_date, days_since_enrolment, days_since_last_episode, starts_with("joint"), ibd_range, CI_lower, CI_upper, sig_est) %>%
ggplot(aes(x = reorder(pair, episode_number), y = joint_probability, group = episode_number, fill = joint_classification)) +
geom_bar(stat = "identity") +
scale_fill_manual(values = c("Relapse" = "turquoise3",
"Recrudescence" = "skyblue4",
"Reinfection" = "magenta3")) +
scale_x_discrete(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0)) +
labs(x = "Sample pair",
y = "Posterior joint probability (highest ranking classification)",
fill = "") +
theme_bw() +
facet_wrap(~ibd_range, scales = "free_x") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
confusion_data_ibsrange <- classification_summary %>%
select(subject_id, sample_id, episode_number, visit_date, days_since_enrolment, days_since_last_episode, starts_with("posterior_"), starts_with("joint"), prop_same_haps_1minusPNH, pnh_range, ibs_range,
# renaming for clarity
pnh_classification = classification) %>%
pivot_longer(cols = (starts_with("posterior")),
names_to = "marginal_classification",
values_to = "marginal_probability") %>%
mutate(marginal_classification = case_when(marginal_classification == "posterior_probability_recrudescence" ~ "Recrudescence",
marginal_classification == "posterior_probability_relapse" ~ "Relapse",
marginal_classification == "posterior_probability_reinfection" ~ "Reinfection"),
marginal_classification = factor(marginal_classification,
levels = c("Relapse", "Recrudescence", "Reinfection")),
marginal_probability_range = cut(marginal_probability,
breaks = c(0, 0.25, 0.5, 0.75, 1),
include.lowest = TRUE,
labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1")),
joint_probability_range = cut(joint_probability,
breaks = c(0, 0.25, 0.5, 0.75, 1),
include.lowest = TRUE,
labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1"))) %>%
select(subject_id, episode_number, days_since_enrolment, ibs_range, pnh_classification, marginal_classification, marginal_probability, marginal_probability_range, joint_classification, joint_probability, joint_probability_range) %>%
# now only keep the highest 'ranking' marginal classification
group_by(subject_id, episode_number) %>%
slice_max(order_by = marginal_probability, n = 1, with_ties = F) %>%
ungroup() %>%
group_by(ibs_range, marginal_classification, joint_classification) %>%
tally() %>%
dcast(ibs_range ~ marginal_classification + joint_classification, value.var = "n", fill = 0)
confusion_data_ibsrange %>%
melt(id.vars = "ibs_range") %>%
ggplot(aes(ibs_range, variable, fill = value)) +
geom_tile() +
scale_fill_gradient(low = "white", high = "red") +
geom_text(aes(label = value)) +
theme_minimal() +
labs(x = "IBS range (1-PNH)",
y = "Marginal vs joint classification",
fill = "Count")
# Create a confusion matrix for each pair
confusion_data_pnhstate <-
classification_summary %>%
select(subject_id, sample_id, episode_number, visit_date, days_since_enrolment, days_since_last_episode, starts_with("posterior_"), starts_with("joint"), prop_same_haps_1minusPNH, pnh_range, ibs_range,
# renaming for clarity
pnh_classification = classification) %>%
pivot_longer(cols = (starts_with("posterior")),
names_to = "marginal_classification",
values_to = "marginal_probability") %>%
mutate(marginal_classification = case_when(marginal_classification == "posterior_probability_recrudescence" ~ "Recrudescence",
marginal_classification == "posterior_probability_relapse" ~ "Relapse",
marginal_classification == "posterior_probability_reinfection" ~ "Reinfection"),
marginal_classification = factor(marginal_classification,
levels = c("Relapse", "Recrudescence", "Reinfection")),
marginal_probability_range = cut(marginal_probability,
breaks = c(0, 0.25, 0.5, 0.75, 1),
include.lowest = TRUE,
labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1")),
joint_probability_range = cut(joint_probability,
breaks = c(0, 0.25, 0.5, 0.75, 1),
include.lowest = TRUE,
labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1")),
# pnh_state = case_when(pnh_classification == "Heterologous" ~ "Reinfection",
# pnh_classification == "Homologous" ~ "Relapse or Recrudescence")
pnh_state = case_when(ibs_range == "0-0.25" | ibs_range == "0.25-0.5" ~ "Reinfection",
(ibs_range == "0.5-0.75" | ibs_range == "0.75-1") ~ "Relapse or Recrudescence")
) %>%
select(subject_id, episode_number, days_since_enrolment, ibs_range, pnh_classification, pnh_state, marginal_classification, marginal_probability, marginal_probability_range, joint_classification, joint_probability, joint_probability_range) %>%
# now only keep the highest 'ranking' marginal classification
group_by(subject_id, episode_number) %>%
slice_max(order_by = marginal_probability, n = 1, with_ties = F) %>%
ungroup() %>%
group_by(pnh_state, marginal_classification, joint_classification) %>%
tally() %>%
dcast(pnh_state ~ marginal_classification + joint_classification, value.var = "n", fill = 0)
confusion_data_pnhstate %>%
melt(id.vars = "pnh_state") %>%
ggplot(aes(pnh_state, variable, fill = value)) +
geom_tile() +
scale_fill_gradient(low = "white", high = "red") +
geom_text(aes(label = value)) +
theme_minimal() +
labs(x = "PNH classification",
y = "Marginal vs joint classification",
fill = "Count")